Speech-processing system

ABSTRACT

A system may receive audio data that represents a wakeword associated with a first speech-processing system and a command associated with a second speech-processing system. Different indications of handing the audio data off to the second speech-processing system may be determined based on a determined amount of interaction with the second speech-processing system. If the amount of interaction is low, a longer, more detailed indication is generated; if the amount of interaction is high, a brief, less detailed indication is generated. A local device may output audio corresponding to the indication before outputting audio generated by the second speech-processing system in response to the command.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims the benefit of priorityunder 35 U.S.C. § 120 to U.S. application Ser. No. 16/571,787, filedSep. 16, 2019, and entitled SPEECH-PROCESSING SYSTEM, in the name ofTimothy Whalin, the entire contents of which are incorporated herein byreference.

BACKGROUND

Speech-processing systems allow human users to control computing devicesusing their voices. These systems identify words spoken by the userbased on properties of received audio input that represents humanspeech. Automatic speech-recognition (ASR) processing combined withnatural-language understanding (NLU) processing allows a system todetermine text corresponding to the speech and to understand an intentexpressed in the speech. ASR processing and NLU processing may becombined with text-to-speech (TTS) processing, which may be used togenerate synthesized speech responsive to the human speech, in aspeech-processing system. Speech processing may be used by computers,hand-held devices, telephone computer systems, kiosks, and other devicesto improve human-computer interactions.

BRIEF DESCRIPTION OF DRAWINGS

For a more complete understanding of the present disclosure, referenceis now made to the following description taken in conjunction with theaccompanying drawings.

FIG. 1 illustrates a system configured to process user input and outputresponse(s) using different speech-processing configurations accordingto embodiments of the present disclosure.

FIGS. 2A-2E are conceptual diagrams of components of configurations of aspeech-processing configurations according to embodiments of the presentdisclosure.

FIG. 3 illustrates a vehicle-based user interface according toembodiments of the present disclosure.

FIGS. 4A, 4B, 4C, 4D, and 4E are diagrams illustrating use of aspeech-processing configurations according to embodiments of the presentdisclosure.

FIG. 5 is a conceptual diagram of natural language processing componentsaccording to embodiments of the present disclosure.

FIG. 6 is a conceptual diagram of natural language processing accordingto embodiments of the present disclosure.

FIG. 7A is a conceptual diagram of text-to-speech components accordingto embodiments of the present disclosure.

FIG. 7B is a conceptual diagram of a speech model according toembodiments of the present disclosure.

FIG. 8 is a conceptual diagram of a user recognition according toembodiments of the present disclosure.

FIG. 9 is a block diagram conceptually illustrating example componentsof a device according to embodiments of the present disclosure.

FIG. 10 is a block diagram conceptually illustrating example componentsof a server according to embodiments of the present disclosure.

FIG. 11 illustrates an example of a computer network for use with thesystem according to embodiments of the present disclosure.

DETAILED DESCRIPTION

Automatic speech recognition (ASR) is a field of computer science,artificial intelligence, and linguistics that relates to transformingaudio data representing speech into text data representing that speech.Natural-language understanding (NLU) is a field of computer science,artificial intelligence, and linguistics that relates to enablingcomputers to derive meaning from the text data. Text-to-speech (TTS) isa field of computer science, artificial intelligence, and linguisticsthat relates to enabling computers to convert a representation of textinto audio representing synthesized speech. ASR, NLU, and/or TTS may beused together as part of a speech-processing system.

A local user device and/or a remote system may be configured to receivea spoken user input and, using ASR, detect a wakeword and/or other textin the user input; using NLU, determine a command in the user input;and, using TTS, provide a response to the command. In some embodiments,in response to the local user device detecting the wakeword, the localuser device may send audio data, representing the user input, to theremote system for further processing (e.g., speech processing). Theremote system may further process the audio data to verify that itincludes a representation of the wakeword and to determine the commandand action. The local user device may then receive, from the remotedevice, output audio, video, or other data related to the action and/orother data required to perform the action.

The wakeword may be, for example, “Alexa.” For example, the local deviceand/or remote system may be configured may be configured to outputweather information in response to user speech including “Alexa, what isthe weather.” The local device and/or remote system may recognize morethan one wakeword; for example, the local device and/or remote systemmay be further configured to perform an action, such as lowering awindow of an automobile, in response to speech from a user including“SmartCar, roll down my window.” In this example and throughout thepresent disclosure, the wakeword “SmartCar” is used to represent awakeword corresponding to a speech-processing configurations of anautomobile; this speech-processing configurations may be capable ofperforming automobile-specific actions, such as raising/lowering carwindows, adjusting car seats, etc. The present disclosure is not,however, limited to only this wakeword (or to only the “Alexa” wakeword)nor to automobiles.

Different wakewords may correspond to different speech-processingconfigurations. In some embodiments, different speech-processingconfigurations have different speech-processing components such asdifferent ASR, NLU, and TTS components. In other embodiments, differentspeech-processing configurations share some speech-processingcomponents, such as ASR and/or NLU components, but have other differentcomponents, such as TTS components. In still other embodiments,different speech-processing configurations share more processingcomponents, such as ASR, NLU, and TTS components but configure one ormore of those components to behave differently, such as changing TTSconfiguration data for different speech-processing configurations. Thus,based on a received indication of a speech-processing configuration,such as the wakeword spoken, one or more speech-processingconfigurations such as ASR, NLU, and/or TTS components, may be selectedor configured for performing speech processing. Different ASR, NLU,and/or TTS components or different configurations of those componentsmay have different sets of corresponding capabilities or actions thatmay be performed in response to receiving the user input. For example,different TTS components or different configurations of those componentsmay be associated with different speech styles for purposes of TTSprocessing. As another example, different ASR, NLU, and/or TTScomponents or different configurations of those components maycorrespond to different feedback mechanisms, such as customized sounds,that are output for each component, or may correspond to customizedlight emitting diode (LED) colors used for each component, etc. Theavailability of different speech-processing configurations—which may beactivated using different wakewords, may perform different actions, mayhave different TTS voices, and/or may have different feedbackmechanisms—may allow a speech-processing configuration to provide acustomer experience of having different “personalities” of differentspeech-processing configurations depending on (e.g.) the wakeword andrequest of a particular utterance.

In some situations, the user input includes a first wakeword associatedwith a first speech-processing configuration, but the command may bebetter handled by a second speech-processing configuration. In someinstances, the first speech-processing configuration may determine thatit is incapable of processing the command and/or performing anassociated action. In these embodiments, the system is able to “handoff” utterances from one speech-processing configuration to another,while also, in some embodiments, indicating to the user that such ahand-off has happened. For example, a local computing device may receiveaudio data corresponding to an utterance, determine a first wakewordrepresented in the utterance, and send audio data (and, in someembodiments, an indication of the wakeword) for the utterance to theremote system for processing. The remote system to which the audio datais sent may depend on the wakeword detected. Alternatively, the remotesystem may select different speech-processing configurations to processthe utterance depending on the indicated wakeword and/or other input.The remote system may instead determine the wakeword or verify therepresentation of the wakeword.

The audio data may thus be sent to a first speech-processingconfiguration corresponding to the wakeword, which may process the audiodata using ASR and/or NLU to determine an appropriate response to thecommand represented in the audio data. The first speech-processingconfiguration, however, may determine that the particular command is tobe better handled by a second speech-processing configuration. Thus therequest may be instead passed to the second-speech-processingconfiguration for processing and response. The user may be notified thatthe request is being passed to the second speech-processingconfiguration, for example in a TTS output in the speech stylecorresponding to the first speech-processing configuration saying thatthe request is being transferred or in a TTS output in the speech styleof the second speech-processing configuration saying that the requestwas transferred. In some embodiments, the response may be generated byboth speech-processing configuration if, for example, the command orrequest corresponds to a first application corresponding to the firstspeech-processing configuration and a second application correspondingto the second speech-processing component. Thus, even though the usermay have intended to invoke the first speech-processing configuration,the request may be handled in full or in part by the secondspeech-processing configuration.

The system may inform the user that the command was passed from thefirst speech-processing configuration to the second speech-processingconfiguration. When the appropriate speech-processing component, such asan NLU component, generates response data corresponding to a response tothe command from the user, the speech-processing component may include,in the response data, a first indication of the identity of the firstspeech-processing component, such as its name, a second indication ofthe identity of the second speech-processing component, such as itsname, and/or indication of the passing from one component to the other.For example, the user may utter speech including a command and awakeword, such as “Alexa,” corresponding to a first, general-purposespeech-processing configuration, but the system may determine that thecommand should be or must be carried out by a second, specializedspeech-processing configuration, such as a second speech-processingconfiguration for an automobile. If the utterance is, for example,“Alexa, roll down my window,” the speech-processing configuration maydetermine that the first speech-processing component is not capable ofrolling down the window and that the second speech-processing componentis capable of rolling down the window. The speech-processingconfiguration may thus cause a local device to output audio thatincludes the name of the second speech-processing component, such as“SmartCar.” For example, the output audio may be “SmartCar can handlethat.”

In various embodiments of the present disclosure, the response data mayinclude different indications of the handoff to the secondspeech-processing configuration. A first indication may include a longindication of the handoff that includes a detailed (e.g., more verbose)description of the handoff. For example, this first indication may be“SmartCar can do that for you. I am handing you over to SmartCar.” Thisfirst indication may be desirable to a user who is unfamiliar with theidea of the handoff to the second speech-processing configuration andwho may be confused by the handoff. Such a user may be one who is new to(e.g., lacks experience in) use of the first and/or secondspeech-processing configurations. The user may further wish to not use,or limit use of, the second speech-processing configuration and may thuswish to be informed when the second speech-processing configuration isbeing used. The user may, for example, be concerned about the secondspeech-processing configuration having access to information about theuser, such as a command history associated with the user.

The indications of the handoff may, however, be shorter. If the userfrequently uses the second speech-processing configuration usingcommands that include the wakeword of the first speech-processingconfiguration, the longer indication may become less necessary becausethe user is familiar with the handoff, and the longer indication maybecome annoying to the user or otherwise undesirable. The shorterindication of the handoff may thus be, for example, “SmartCar can dothat for you.” Upon further use, the indication may become stillshorter; for example, “OK, here's SmartCar.” The indication may, in someembodiments, may not include a representation of the name of the secondspeech-processing configuration; for example, the indication may simplybe “OK.”

The length of the indication may correspond to an interaction score. Theinteraction score may be determined based on a number of interactionswith the first speech-processing configuration, a number of interactionswith the second speech-processing configuration, and/or a number ofhandoffs from one speech-processing configuration to the other. Theinteraction score may be higher (e.g., indicating a greater familiarity)as the number of interactions increases. The interaction score mayfurther be based on the timing of the interactions; interactions thatoccurred more recently in time may increase the interaction score morethan interactions that occurred less recently in time. The interactionscore may further be based on feedback from the user; this feedback maybe input to a user profile indicating a preference for longer or shorterindications. The feedback may instead or in addition be verbal feedbackuttered by the user (e.g., “OK, I get it already”) and/or non-verbalsounds by the user (e.g., “ugh”) made during or after the command.

FIG. 1 illustrates a system configured to determine a response to acommand or request represented in audio data in accordance with thepresent disclosure. Although the figures and discussion of the presentdisclosure illustrate certain operational steps of the system in aparticular order, the steps described may be performed in a differentorder (as well as certain steps removed or added) without departing fromthe intent of the disclosure.

In various embodiments, a local user device 110 such as smart speaker110 a, a vehicle 110 b, or other device communicates with a remotesystem 120 using a network 199. While FIG. 1 illustrates a smart speaker110 a and a vehicle 110 b, the disclosure is not limited thereto, andthe systems and methods described herein may be implemented using otherlocal devices 110, such as smartphones, tablet computers, personalcomputers, or other devices. The system 120 receives (130) first audiodata including a representation of utterance, the utterance including arepresentation of a wakeword and a representation of a command. Thesystem 120 determines (132) that the wakeword is associated with a firstspeech-processing configuration. The system 120 determines (134) thatthe command is associated with a second speech-processing configuration.The system 120 determines a user account associated with the first audiodata and determines (136), based at least in part on the user account, ascore corresponding to interaction with a second speech-processingconfiguration. The system 120 determines (138), using the firstspeech-processing configuration and the score, first output dataindicating that the command is associated with the secondspeech-processing configuration. The system 120 determines (140), usingthe second speech-processing configuration and the score, second outputdata corresponding to a response to the command.

The system may operate using various components as described in FIGS.2A-2E. The various components may be located on same or differentphysical devices. Communication between various components may occurdirectly (via, e.g., a bus connection) or across a network(s) 199.Referring first to FIG. 2A, as described in greater detail below, thelocal device 110 may include a wakeword detector 220 for detecting oneor more wakewords, a voice activity detector 222 for detecting anutterance, and/or one or more applications 224 for providing outputand/or changing a state of the local device 110, such as illuminating alight.

An audio capture component(s), such as a microphone or array ofmicrophones of the local device 110, captures input audio 11 and createscorresponding input audio data 211. A wakeword detector 220 of the localdevice 110 may process the input audio data 211 to determine whetherspeech is represented therein. The local device 110 may use varioustechniques to determine whether the input audio data 211 includesspeech. In some examples, a voice-activity detector 222 of the localdevice 110 may apply voice-activity detection (VAD) techniques. Suchtechniques may determine whether speech is present in audio data basedon various quantitative aspects of the input audio data 211, such as thespectral slope between one or more frames of the audio data; the energylevels of the audio data in one or more spectral bands; thesignal-to-noise ratios of the audio data in one or more spectral bands;or other quantitative aspects. In other examples, the local device 110may implement a classifier configured to distinguish speech frombackground noise. The classifier may be implemented by techniques suchas linear classifiers, support vector machines, and decision trees. Instill other examples, the local device local device 110 may apply hiddenMarkov model (HMM) or Gaussian mixture model (GMM) techniques to comparethe audio data to one or more acoustic models in storage, which acousticmodels may include models corresponding to speech, noise (e.g.,environmental noise or background noise), or silence. Still othertechniques may be used to determine whether speech is present in audiodata.

The wakeword detector 220 may determine that the input audio data 211contains a representation of a wakeword (as described in greater detailbelow); the local device 110 may thereafter send the input audio data211 and/or an indication of the wakeword to the system(s) 120. Asdescribed above, an example wakeword is “Alexa” or “SmartCar.” The localdevice 110 may instead or in addition send the audio data to thesystem(s) 120 when an input detector detects an input—such as a keypress, button press, or touch-screen touch. An example button is a “Pushto Talk” button. In either event, the local device 110 sends the inputaudio data 211 and/or indication of the input to the server 120.

The wakeword detector 220 may compare audio data to stored data todetect a wakeword. One approach for wakeword detection applies generallarge vocabulary continuous speech recognition (LVCSR) systems to decodeaudio signals, with wakeword searching being conducted in the resultinglattices or confusion networks. LVCSR decoding may require relativelyhigh computational resources. Another approach for wakeword detectionbuilds HMMs for each wakeword and non-wakeword speech signals,respectively. The non-wakeword speech includes other spoken words,background noise, etc. There can be one or more HMMs built to model thenon-wakeword speech characteristics, which are named filler models.Viterbi decoding is used to search the best path in the decoding graph,and the decoding output is further processed to make the decision onwakeword presence. This approach can be extended to includediscriminative information by incorporating a hybrid DNN-HMM decodingframework. In another example, the wakeword detector 220 may be built ondeep neural network (DNN)/recursive neural network (RNN) structuresdirectly, without HMM being involved. Such an architecture may estimatethe posteriors of wakewords with context information, either by stackingframes within a context window for DNN, or using RNN. Follow-onposterior threshold tuning or smoothing is applied for decision making.Other techniques for wakeword detection, such as those known in the art,may also be used.

If the wakeword is detected by the wakeword detector 220 and/or input isdetected by the input detector, the local device 110 may transmit theaudio data 211 and/or indication of the wakeword or input to thesystem(s) 120. The input audio data 211 may include data correspondingto the wakeword; in other embodiments, the portion of the audiocorresponding to the wakeword is removed by the local device 110 priorto sending the input audio data 211 to the system(s) 120. In the case oftouch input detection, for example, the input audio data 211 may notinclude a wakeword.

Regarding the wakeword detector 220, Viterbi decoding may be performedfor a competing foreground wakeword path and background speech/nonspeechpath, and wakeword hypothesis may be triggered when a log-likelihoodratio of the foreground path versus the background path exceeds apredetermined threshold. Once the ratio exceed the predeterminedthreshold, features may be extracted from the audio data and fed intoone or more second stage classifiers, which could be a support vectormachine (SVM) or deep neural network (DNN).

In various embodiments, the wakeword detector 220 may use one of aplurality of wakeword-detection models. The wakeword detector 220 may,for example, be implemented on a digital signal processor (DSP) thatincludes an interface, such as an application-programming interface(API), that allows communication with another system or device. In someembodiments, the wakeword detector 220 includes a plurality ofwakeword-detection models; in these embodiments, the other device orsystem sends a command, via the API, to instruct the wakeword detector220 to use a different wakeword-detection model but need not send datacorresponding to the model.

The local device 110 may select a new wakeword-detection model based onthe location of the local device 110. The local device 110 may determineits location by processing input from one or more input devices. Theseinput devices may include, for example, a camera, a microphone, anaccelerometer, a gyroscope, a biometric sensor, a global positioningsystem (GPS), a thermometer, an antenna, or other such sensors. Thelocal device 110 may instead or in addition determine its location byreceiving location data from another device, such as the server 120.

The local device 110 may process input from multiple sensors todetermine the location. For example, if the GPS or accelerometer of thelocal device 110 indicates a speed corresponding to travel in a vehicle,the local device 110 may process input from an additional sensor (e.g.,a microphone or camera) to determine the type of vehicle and itscorresponding location. If, for example, the audio data includes arepresentation of road noise but not a representation of speech frommultiple speakers, the local device 110 may determine that the vehicleis an automobile and that the location is a private location. If,however, the audio data includes a representation of speech frommultiple speakers, the local device 110 may determine that the vehicleis a bus or train and that the location is a public location. Similarly,the local device 110 may process image data captured by the camera andcompare the captured image data to stored image data of automobiles andbusses to determine the type of vehicle.

The wakeword-detection models may be implemented for their correspondinglocations via training, as described herein, using location-specifictraining data. For example, the home-location wakeword-detection modelmay be trained using speech data corresponding to speech of a userand/or family member; home-location wakeword-detection model may insteador in addition be trained using speech data from other persons to, forexample, distinguish between adult and child voices. The public-locationwakeword-detection model may be trained using speech data correspondingto the wakeword being uttered in a noisy location. Thewakeword-detection models corresponding to other locations may betrained using data corresponding to their locations.

In various embodiments, the wakeword-detection model of the wakeworddetector 220 is implemented to detect wakewords spoken in differentaccents corresponding to different countries, regions, or other areas.For example, the wakeword-detection model may be implemented to detectthe wakeword “Alexa” whether it is spoken in an Indian, Scottish, orAustralian accent. The wakeword-detection model may be also implementedto detect other wakewords in other languages; these other languages mayhave similar variations in accents that the wakeword-detection model maybe similarly implemented to detect.

The wakeword detector 220 may determine a similarity score for thecandidate wakeword based on how similar it is to the stored wakeword; ifthe similarly score is higher than the wakeword-detection threshold, thewakeword detector 220 determines that the wakeword is present in theaudio data, and if the similarity score is less than thewakeword-detection threshold, the wakeword detector 220 determines thatthe wakeword not is present in the audio data. For example, if thecandidate wakeword matches the stored wakeword very closely, thewakeword detector 220 may determine a similarity score of 100; if thecandidate wakeword does not match the stored wakeword at all, thewakeword detector 220 may determine a similarity score of 0. If thewakeword detector 220 determines candidate wakeword partially matchesthe stored wakeword, it may determine an intermediate similarity score,such as 75 or 85.

Though the disclosure herein describes a similarity score of zero to100—wherein zero is least similar and 100 is most similar—and though thefollowing examples carry through this type of similarity score, thepresent disclosure is not limited to any particular range of values ofthe similarity score, and any system or method of determining similaritybetween a candidate wakeword represented in captured audio data and astored representation of the wakeword is within the scope of the presentdisclosure.

The local device 110 may also use different wakewords for differentskills within a same speech-processing configuration. For example, auser may speak “SmartCar” as a special wakeword to invoke a specificskill or processing speech-processing configuration within a firstspeech-processing configuration (e.g., a speech-processing configurationthat may otherwise be invoked by speaking “Alexa”). Use of the special“SmartCar” wakeword may result in different routing of the utterancethrough the first speech-processing configuration than use of anotherwakeword such as “Alexa.” Thus the local device 110 using the techniquesdescribed herein may process incoming audio to determine a firstconfidence that a detected wakeword is a first wakeword associated witha first speech-processing configuration (which may be include firstspeech processing component (e.g., TTS component, skill, etc.) withinthe first speech-processing configuration) as well as determine a secondconfidence that the detected wakeword is a second wakeword associatedwith a second speech-processing configuration (which may be a secondspeech-processing configuration within the second speech-processingconfiguration. The different configuration may be associated withdifferent ASR processing, different NLU processing, different TTSprocessing, different commands, or other differences.

Upon receipt by the system(s) 120, the input audio data 211 may be sentto an orchestrator component 240. The orchestrator component 240 mayinclude memory and logic that enables the orchestrator component 240 totransmit various pieces and forms of data to various components of thesystem 120, as well as perform other operations as described herein.

The orchestrator component 240 may, for example, send the input audiodata 211 to a speech-processing configuration manager 294 and/or one ormore of the speech-processing configuration 292, which may be used todetermine which, if any, of the ASR 250, NLU 260, and/or TTS 280components should receive and/or process the audio data 211. In someembodiments, the speech-processing configuration manager 294 includesone or more ASR components 250, NLU components 260, TTS components 280,and/or other processing components, and processes the input audio data211 before sending it and/or other data to one or more speech-processingcomponents 292 for further processing. In other embodiments, theorchestrator component 240 sends the input audio data 211 directly toone or more of the speech-processing configuration 292 for processing.

As described above, the speech-processing configuration manager 294(and/or one or more of the speech-processing configurations 292) maydetermine that the input audio data 211 includes a representation of awakeword associated with a first speech processing configuration 292 aand a command associated with a second speech-processing configuration292 b. For example, the wakeword may be “Alexa,” and the command may be“roll down my window.” The wakeword “Alexa” may be associated with afirst speech-processing configuration 292 a, while the command “rolldown my window” may be associated with a second speech-processingconfiguration 292 b. The first speech-processing configuration 292 a maygenerate output data that includes acknowledgement of receipt of theinput audio data 211 and/or an indication that the secondspeech-processing configuration 292 b will further process the inputaudio data 211. The second speech-processing configuration 292 b mayfurther process the input audio data 211 and determine response data,which may include additional audio data and/or other data correspondingto an action related to the response. The system 120 may send responseaudio data 212 that includes the indication and/or additional audio datato the local device 110, which may output corresponding output audio 12.The system 120 may further send additional data to the local device 110corresponding to an action responsive to the input audio data 211, suchas a command to roll down the window of the vehicle.

As described above, the indication that the second speech-processingconfiguration 292 b will further process the input audio data 211 mayvary. If, for example, a user has a low level of interaction with thesecond speech-processing configuration 292 b, the indication may be longand include information such as the name of the second speech-processingconfiguration 292 b, an explanation of the handoff to the secondspeech-processing configuration 292 b, and/or additional capabilities ofthe second speech-processing configuration 292 b. If the user has amedium level of interaction with the second speech-processingconfiguration 292 b, the indication may be shorter and omit some of thatinformation, such as the additional capabilities of the secondspeech-processing configuration 292 b. If the user has a high level ofinteraction with the second speech-processing configuration 292 b, theindication may be brief and include only the name of the secondspeech-processing configuration 292 b or, in some embodiments, omit eventhe name and any indication of the handoff. The score may be, forexample, between 1 and 5, wherein 1 represents a low amount ofinteraction and 5 represents a high degree of interaction.

The speech-processing configuration manager 294 (and/or one or more ofthe speech-processing configurations 292) may thus include aninteraction determination component 296 that determines an interactionscore corresponding to an amount of previous interaction(s) between aspeaker and the second speech-processing configuration 292 b. Theinteraction determination component 296 may determine that a profile isassociated with the input audio data 211 using, as described in greaterdetail below, a speaker-recognition component 295. The profile mayindicate historical interaction with the second speech-processingconfiguration 292 b; this history may include a number of times that thesystem 120 has received input audio data corresponding to a particularcommand associated with the second speech-processing configuration 292b. Each indication of receipt may further include a time of receipt ofthe command. The history may further include a number of times, andtimes of receipt, of other interactions with the secondspeech-processing configuration that included other commands.

The interaction determination component 296 may thus determine theinteraction score based on the number of times that the command has beenpreviously received; more receipts may mean a higher score. For example,if the command has been received less than 5 times, the score may be 1;between 5 and 10 times may mean a score of 2; and so on. The interactiondetermination component 296 may further determine the score based on thenumber of times that different but related commands have been received.The related commands may be related because they are also associatedwith the second speech-processing configuration 292 b or because theyare similar in function. The interaction determination component 296 maydetermine that related commands have less influence on the score thanreceipt of the actual command does. For example, the score may be 2 if arelated command is received between 10 and 15 times (as opposed to ascore of 2 for receipt of the actual command between 5 and 10 times).

The interaction determination component 296 may further determine thescore based on the timing of receipt of the commands or relatedcommands. The interaction determination component 296 may determine atime of receipt of a previously received command, the time of receipt ofa present command, and then compare the difference to a threshold. Ifthe difference is greater than the threshold, the interactiondetermination component 296 may not change the score based on thepreviously received command. The interaction determination component 296may instead or in addition weight the effect of a previously receivedcommand on the score in accordance with how recently the command wasreceived, wherein more recently received commands have a greater effecton the score than less recently received commands. The interactiondetermination component 296 may decrease the score from a previous,higher value if an amount of time greater than the threshold haselapsed. Thus, if a user hasn't used the command in an amount of timegreater than the threshold, the interaction determination component 296will generate a score indicating a longer indication of the handoff toaccount for the fact that the user may have forgotten the use of thesecond speech-processing configuration 292 b.

In some embodiments, the interaction determination component 296 adjuststhe score based on one or more additional factors. For example, thescore may be increased if the interaction determination component 296determines that the audio data represents an urgent request, such as acall for an ambulance. Similarly, the interaction determinationcomponent 296 may increase the score if it determines that an emotionalstate of a user is excited, upset, or otherwise other than calm. Theinteraction determination component 296 may lower the score if itdetermines that an age of the user is very young (e.g., less than 8) orvery old (e.g., older than 80).

After the interaction determination component 296 determines the score,it may send the score to one or more speech-processing configuration 292and/or skills 290. The speech-processing configuration manager 294,speech-processing configuration 292, and/or skills 290 may then generatetext that includes a description of the handoff in accordance with thescore. Generation of this text may include selecting text correspondingto the score from a predetermined set of candidate responses and/ordetermining the text using a speech model. A TTS component 280 maygenerate speech using this text, and associated response audio data 212may be sent to the local device 110 to be output as output audio 12. Thesecond speech-processing component 292 may thereafter process the inputaudio data 211 to determine further response data, which may besimilarly sent to the local device 110. In some embodiments, the outputdata corresponding to the handover indication and the output datacorresponding to the response are combined before sending to the localdevice 110.

A permission determination component 297 may first determine whether agrant of permission is required for the second speech-processingcomponent 292 a to process the input audio data 211. This requirementmay be based on whether the input audio data 211 includes anyinformation that the user may wish to be protected, such as names,addresses, or account numbers. The requirement may also or instead bebased on the type of the second speech-processing configuration 292 a,such as whether it has access to personal information or bank accounts.The permission determination component 297 may further determine if theuser has granted permission for the second speech-processing component292 a to process the input audio data 211. The user account may includean indication of the grant, which may be a grant limited to only one ora subset of the speech-processing configuration 292. If the grant isrequired but no indication is present in the user account, thepermission determination component 297 may cause the system 120 and/orlocal device 110 to prompt the user and ask for permission. If the useracquiesces, the second speech-processing configuration 292 b processesthe input audio data 211 as described above. If the user declines, anerror is returned; the error may be, for example, audio indicating thatthe command cannot be carried out.

The interaction determination component 296 may update a user profile ofthe user to reflect receipt of the command and the outputting of theindication of the handover. The speaker-recognition component 295 mayfurther indicate the presence of a second user proximate the localdevice 110 by processing the input audio data 211. The input audio data211 may include, for example, a representation of second speech of thesecond user and/or other noises that identify the second user. Theinteraction determination component 296 may thus determine that thesecond user also hears output audio 12 that describes the handoff to thesecond speech-processing configuration 292 b and may thus update asecond user profile of the second user to reflect this hearing. When andif the second user utters a command that involves a handoff to thesecond speech-processing configuration 292 b, the score computed for thesecond user may be based at least in part on this second-handinteraction. This second-hand interaction of the indication may,however, have less of an effect on computing an interaction score forthe second user than the above-described first-hand interaction. Forexample, if a first-hand interaction affect the score with a weight of1.0, a second-hand interaction may affect the score with a weight of 0.5(e.g., carry half the weight of a first-hand interaction).

The first speech-processing configuration 292 a may control, have accessto, or otherwise be associated with a first set of skills, applications,and speech styles, and the second speech-processing configuration 292 bmay control, have access to, or otherwise be associated with a secondset of skills, applications, and speech styles. The first and second setof skills may include common skills as well as skills exclusive to eachspeech-processing configuration 292. In some embodiments, a firstspeech-processing configuration 292 a is a general-purposespeech-processing configuration and may provide such skills andapplications as weather forecasts, restaurant reservations, shoppingservices, and Internet searches; a second speech-processingconfiguration 292 b is a vehicle-specific speech-processingconfiguration and may provide such skills and applications as changing astate of the local device 110 (e.g., raising/lowering a window, settinga thermostat, and adjusting a seat) and/or providing diagnosticinformation. Some applications and skills may be common to bothspeech-processing configuration 292 (e.g., playing music or providingnavigation information). Any number of speech-processing configuration292, however, having any type of applications or skills is within thescope of the present disclosure.

The speech-processing configuration manager 294 directs processing ofthe input audio data 211 using, in some embodiments, one or morecomponents in a speech-processing configuration 292, such as an ASRcomponent 250 and an NLU component 260. In other embodiments, thespeech-processing configuration manager 294 includes an NLU componentfor NLU processing; this built-in NLU may be used to process all audiodata, while different NLU components 260 in the speech-processingconfiguration 292 may be used to process different audio data (and maybe selected based on a wakeword detected in the audio data). Asexplained in greater detail below, the speech-processing configurationmanager 294 and/or speech-processing configuration 292 may perform ASRand/or NLU processing to determine a domain, intent, and/or meaningcorresponding to the audio data 211. The speech-processing configurationmanager 294 and/or speech-processing configuration 292 may instead or inaddition identify keywords in the input audio data 211 and identify adomain, intent, and/or meaning corresponding to the keywords. Thespeech-processing configuration manager 294 and/or speech-processingconfiguration 292 may further identify a user associated with anutterance in the input audio data 211 and identify a domain, intent,and/or meaning corresponding to the user. The speech-processingconfiguration manager 294 and/or speech-processing configuration 292 mayfurther identify a domain, intent, and/or meaning associated with theinput audio data 211 based on information in a user profile associatedwith the user (such as usage history information), a location of thelocal device 110, a time of day, week, month, or year, and/ortemperature information.

The speech-processing configuration manager 294 and/or speech-processingconfiguration 292 may compare a determined domain, intent, and/ormeaning to a list of corresponding applications or skills associatedwith each speech-processing configuration 292. The comparison mayinclude determining a number of whole or partial matches of the domain,intent, and/or meaning present in each list. The speech-processingconfiguration manager 294 and/or speech-processing configuration 292 maydetermine a score for each speech-processing configuration 292corresponding to the ability of each speech-processing configuration 292to respond to a command or request represented in the input audio data211. If the domain, intent, and/or meaning is determined to beassociated with a first speech-processing configuration 292 but not witha second speech-processing configuration 292, the speech-processingconfiguration manager 294 may award the first speech-processingconfiguration 292 a higher score than the second speech-processingconfiguration 292. If the domain, intent, and/or meaning is determinedto be associated both the first speech-processing configuration 292 andthe second speech-processing configuration 292, the speech-processingconfiguration manager 294 may determine the ranking based on other data,such as user identification, user profile data, location, or otherinformation.

In some embodiments, the orchestrator 240 and/or speech-processingconfiguration manager 294 communicate with the speech-processingconfigurations 292 using an application programming interface (API). TheAPI may be used to send and/or receive data, commands, or otherinformation to and/or from the speech-processing configurations 292. Forexample, the orchestrator 240 may send, via the API, the input audiodata 211 to a speech-processing systems elected by the speech-processingconfiguration manager 294 and may receive, from the selectedspeech-processing configuration 292, a command and/or data responsive tothe audio data 211.

In some embodiments, as described above, the speech-processingconfiguration manager 294 includes processing components, such as ASRand/or NLU components, that may be used to select a speech-processingconfiguration 292. Alternatively or in addition, in other embodiments,the speech-processing configuration manager 294 communicates, via theAPI, with a particular speech-processing configuration 292 to cause thespeech-processing configuration 292 to perform the processing, andreceives in response data corresponding to the processing and/or aselected speech-processing configuration 292. The speech-processingconfiguration manager 294 may include, for example, one or moreapplication programming interfaces (APIs) for communicating with aparticular speech-processing configuration 292, a configuration managerfor determining properties of the local device 110, and/or an eventhandler for handling events received from the local device 110 and/orspeech pipelines 292, but may not include an ASR processor or an NLUprocessor, which may be instead included in a particularspeech-processing configuration 292.

After the speech-processing configuration manager 294 selects one ormore speech-processing system(s) 292, the orchestrator component 240 maysend the input audio data 211 to the corresponding speech-processingsystem(s) 292. Each speech-processing configuration 292 may include anASR component 250, which may transcribe the input audio data 211 intotext data. The text data output by the ASR component 250 represents oneor more than one (e.g., in the form of an N-best list) ASR hypothesesrepresenting speech represented in the input audio data 211. The ASRcomponent 250 interprets the speech in the input audio data 211 based ona similarity between the audio data 211 and pre-established languagemodels. For example, the ASR component 250 may compare the input audiodata 211 with models for sounds (e.g., acoustic units such as phonemes,senons, phones, etc.) and sequences of sounds to identify words thatmatch the sequence of sounds of the speech represented in the inputaudio data 211. The ASR component 250 sends the text data generatedthereby to an NLU component 260, via, in some embodiments, theorchestrator component 240. The text data sent from the ASR component250 to the NLU component 260 may include a single top-scoring ASRhypothesis or may include an N-best list including multiple top-scoringASR hypotheses. An N-best list may additionally include a respectivescore associated with each ASR hypothesis represented therein.

Each speech-processing configuration 292 may further include a NLUcomponent 260 that attempts to make a semantic interpretation of thephrase(s) or statement(s) represented in the text data input therein bydetermining one or more meanings associated with the phrase(s) orstatement(s) represented in the text data. The NLU component 260 maydetermine an intent representing an action that a user desires beperformed and may determine information that allows a device (e.g., thelocal device 110, the system(s) 120, a skill component 290, a skillsystem(s) 225, etc.) to execute the intent. For example, if the textdata corresponds to “play Africa by Toto,” the NLU component 260 maydetermine an intent that the system output music and may identify “Toto”as an artist and “Africa” as the song. For further example, if the textdata corresponds to “what is the weather,” the NLU component 260 maydetermine an intent that the system output weather informationassociated with a geographic location of the local device 110. Inanother example, if the text data corresponds to “turn off the lights,”the NLU component 260 may determine an intent that the system turn offlights associated with the local device 110 or the user 5.

The NLU results data may be sent from the NLU component 260 (which mayinclude tagged text data, indicators of intent, etc.) to a skillcomponent(s) 290. If the NLU results data includes a single NLUhypothesis, the NLU component 260 may send the NLU results data to theskill component(s) 290 associated with the NLU hypothesis. If the NLUresults data includes an N-best list of NLU hypotheses, the NLUcomponent 260 may send the top scoring NLU hypothesis to a skillcomponent(s) 290 associated with the top scoring NLU hypothesis.

A “skill component” may be software running on the system(s) 120 that isakin to a software application. That is, a skill component 290 mayenable the system(s) 120 to execute specific functionality in order toprovide data or produce some other requested output. The system(s) 120may be configured with more than one skill component 290. For example, aweather service skill component may enable the system(s) 120 to provideweather information, a car service skill component may enable thesystem(s) 120 to book a trip with respect to a taxi or ride sharingservice, a restaurant skill component may enable the system(s) 120 toorder a pizza with respect to the restaurant's online ordering system,etc. A skill component 290 may operate in conjunction between thesystem(s) 120 and other devices, such as the local device 110, in orderto complete certain functions. Inputs to a skill component 290 may comefrom speech processing interactions or through other interactions orinput sources. A skill component 290 may include hardware, software,firmware, or the like that may be dedicated to a particular skillcomponent 290 or shared among different skill components 290.

A skill system(s) 225 may communicate with a skill component(s) 290within the system(s) 120 and/or directly with the orchestrator component240 or with other components. A skill system(s) 225 may be configured toperform one or more actions. An ability to perform such action(s) maysometimes be referred to as a “skill.” That is, a skill may enable askill system(s) 225 to execute specific functionality in order toprovide data or perform some other action requested by a user. Forexample, a weather service skill may enable a skill service(s) 225 toprovide weather information to the system(s) 120, a car service skillmay enable a skill system(s) 225 to book a trip with respect to a taxior ride sharing service, an order pizza skill may enable a skillsystem(s) 225 to order a pizza with respect to a restaurant's onlineordering system, etc. Additional types of skills include home automationskills (e.g., skills that enable a user to control home devices such aslights, door locks, cameras, thermostats, etc.), entertainment deviceskills (e.g., skills that enable a user to control entertainment devicessuch as smart televisions), video skills, flash briefing skills, as wellas custom skills that are not associated with any pre-configured type ofskill.

The system(s) 120 may be configured with a skill component 290 dedicatedto interacting with the skill system(s) 225. Unless expressly statedotherwise, reference to a skill, skill device, or skill component mayinclude a skill component 290 operated by the system(s) 120 and/or skilloperated by the skill system(s) 225. Moreover, the functionalitydescribed herein as a skill or skill may be referred to using manydifferent terms, such as an action, bot, app, or the like.

The speech-processing configuration manager 294 and/or eachspeech-processing configuration 292 may include a TTS component 280 thatgenerates audio data (e.g., synthesized speech) from text data using oneor more different methods. Text data input to the TTS component 280 maycome from a skill component 290, the orchestrator component 240, oranother component of the system. The text data may include an indicationof a handoff, if any, and/or data responsive to a command.

In one method of synthesis called unit selection, the TTS component 280matches text data against a database of recorded speech. The TTScomponent 280 selects matching units of recorded speech and concatenatesthe units together to form audio data. In another method of synthesiscalled parametric synthesis, the TTS component 280 varies parameterssuch as frequency, volume, and noise to create audio data including anartificial speech waveform. Parametric synthesis uses a computerizedvoice generator, sometimes called a vocoder.

The system(s) 120 may include a speaker-recognition component 295 thatrecognizes one or more speakers associated with data input to thesystem. The speaker-recognition component 295 may further identify thata recognized speaker is also associated with a user account. If thespeaker-recognition component 295 is unable to identify an associateduser account either because a speaker did not create such a user accountor because the speaker-recognition component 295 does not have enoughinput data to determine the user account, the speaker-recognitioncomponent 295 may identify or create a speaker account associated withthe identified speaker. The speaker account may include datarepresenting past interactions of the recognized speaker with a localdevice 110 and/or system 120, such as past utterances or locations. Whenand if the speaker-recognition component 295 identifies a user accountassociated with the recognized speaker, the speaker-recognitioncomponent 295 may merge some or all of the data associated with thespeaker account with the user account.

The speaker-recognition component 295 may take as input the audio data211 and/or text data output by the ASR component 250. Thespeaker-recognition component 295 may perform speaker-recognition bycomparing audio characteristics in the input audio data 211 to storedaudio characteristics of users or speakers. The speaker-recognitioncomponent 295 may also perform speaker-recognition by comparingbiometric data (e.g., fingerprint data, iris data, etc.), received bythe system in correlation with the present speaker input, to storedbiometric data of users and/or previous speakers. Thespeaker-recognition component 295 may further performspeaker-recognition by comparing image data (e.g., including arepresentation of at least a feature of a speaker), received by thesystem in correlation with the present user input, with stored imagedata including representations of features of different speaker. Thespeaker-recognition component 295 may perform additionalspeaker-recognition processes.

The speaker-recognition component 295 determines scores indicatingwhether speaker input originated from a particular speaker. For example,a first score may indicate a likelihood that the user input originatedfrom a first speaker, a second score may indicate a likelihood that theuser input originated from a second speaker, etc. Thespeaker-recognition component 295 also determines an overall confidenceregarding the accuracy of speaker-recognition operations.

Output of the speaker-recognition component 295 may include a singlespeaker identifier corresponding to the most likely speaker thatoriginated the user input. Alternatively, output of thespeaker-recognition component 295 may include an N-best list of speakeridentifiers with respective scores indicating likelihoods of respectiveusers originating the user input. The output of the user-recognitioncomponent 295 may be used to inform NLU processing as well as processingperformed by other components of the system. As described above, theinteraction determination component 296 may use this user identifier toidentify a user account in the profile storage 270.

The system(s) 120 may include profile storage 270. The profile storage270 may include a variety of information related to individual speakers,groups of speakers, users, groups of users, devices, etc. that interactwith the system. A “profile” refers to a set of data associated with aspeaker, user, device, etc. The data of a profile may includepreferences specific to the speaker, user, device, etc.; input andoutput capabilities of the device; internet connectivity information;user bibliographic information; subscription information, as well asother information.

The profile storage 270 may include one or more speaker and/or userprofiles, with each speaker and/or user profile being associated with adifferent speaker and/or user identifier. Each speaker and/or userprofile may include various user identifying information. Each speakerand/or user profile may also include preferences of the speaker, user,and/or one or more device identifiers, representing one or more devicesof the user. When a user logs into to an application installed on alocal device 110, the user profile (associated with the presented logininformation) may be updated to include information about the localdevice 110. As described, the profile storage 270 may further includedata that shows an interaction history of a speaker and/or user,including commands and times of receipt of commands. The profile storage270 may further include data that shows when a second speaker and/oruser was present to hear an indication of a handoff for a commanduttered by a first user.

The profile storage 270 may include one or more group profiles. Eachgroup profile may be associated with a different group identifier. Agroup profile may be specific to a group of speakers and/or users. Thatis, a group profile may be associated with two or more individualspeaker and/or user profiles. For example, a group profile may be ahousehold profile that is associated with speaker and/or user profilesassociated with multiple users of a single household. A group profilemay include preferences shared by all the speaker and/or user profilesassociated therewith. Each speaker and/or user profile associated with agroup profile may additionally include preferences specific to thespeaker and/or user associated therewith. That is, each speaker and/oruser profile may include preferences unique from one or more other userprofiles associated with the same group profile. A speaker and/or userprofile may be a stand-alone profile or may be associated with a groupprofile.

The profile storage 270 may include one or more device profiles. Eachdevice profile may be associated with a different device identifier.Each device profile may include various device identifying information.Each device profile may also include one or more speaker and/or useridentifiers, representing one or more speakers and/or users associatedwith the device. For example, a household device's profile may includethe user identifiers of users of the household.

The system may be configured to incorporate user permissions and mayonly perform activities disclosed herein if approved by a user. Asdescribed above, these permissions may include a grant (or denial) touse a particular speech-processing configuration 292. The systems,devices, components, and techniques described herein may thus beconfigured to restrict processing where appropriate and only processuser information in a manner that ensures compliance with allappropriate laws, regulations, standards, and the like. The system andtechniques can be implemented on a geographic basis to ensure compliancewith laws in various jurisdictions and entities in which the componentsof the system and/or user are located.

FIGS. 2B, 2C, 2D, and 2E illustrate further embodiments of the presentdisclosure. In FIG. 2B, the speech-processing configuration(s) 292 wincludes a single ASR component 250 that processes the input audio data211 and a single NLU component 260 that processes the output of the ASRcomponent 250. The speech-processing configuration(s) 292 w include,however, a plurality of TTS components 280 each associated with adifferent speech-processing configuration. A first TTS component 280 maybe used to generate response audio data 212 associated with a firstspeech-processing configuration, such as one associated with a firstwakeword “Alexa,” while a second TTS component 280 may be used togenerate response audio data 212 associated with a second wakeword“SmartCar.” The speech-processing configuration(s) 292 w may similarlyinclude additional TTS components 280 associated with otherspeech-processing configurations.

Referring to FIG. 2C, the speech-processing configuration(s) 292 x mayinclude a single TTS component 280 but multiple configuration profiles298 that configure the TTS component 280 for different speech-processingconfigurations. Each configuration profile 298 may include configurationdata such as TTS model weights or TTS model inputs that configure orinstruct a TTS model to generate response audio data 212 in accordancewith a particular speech-processing configuration. For example, a firstconfiguration profile 298 may be associated with a firstspeech-processing configuration associated with a first wakeword such as“Alexa,” while a second configuration profile 298 may be associated witha second speech-processing configuration associated with a firstwakeword such as “SmartCar.” In some embodiments, the speech-processingconfiguration 292 x includes multiple TTS components 280, such as in theembodiments described above with reference to FIG. 2A, but one or moreof the TTS components 280 are configured using two or more configurationprofiles 298.

Referring to FIG. 2D, some or all of the processing described above maybe performed by the device 110. For example, the interactiondetermination component 296, permission determination component 297,and/or speech-processing configuration 292 y may be disposed in thelocal device 110. In still other embodiments, with reference to FIG. 2E,some or all of these components (such as the interaction determinationcomponent 296, permission determination component 297, and/orspeech-processing configuration 292 za) may be present both on the localdevice 110, and others of these components (such as thespeech-processing configuration 292 zb) may also be present on theremote system 120.

FIG. 3 illustrates an interior of the vehicle 110 a. The vehicle 110 amay include, on a dashboard, steering wheel, heads-up display, or otherinterior surface or feature, such as a display 302, which may be atouchscreen display. The vehicle 110 a may further include one or moremicrophones 304, which may be used to receive audio that includes anutterance and generate corresponding input audio data. One or moreloudspeakers 306 may be used to output audio corresponding to outputaudio data, which may be received from the system 120. One or morelights 308 may be used to display information; in some embodiments, thelights 308 are used to identify a speech-processing system being used toprovide output audio and/or perform a task. For example, one light 308may be illuminated when a first speech-processing system is being usedto output audio and/or perform an action, and a second light 308 may beilluminated when a second speech-processing system is being used. Inanother example, a light 308 is illuminated using a first color (e.g.,blue) when a first speech-processing system is being used, and samelight 308 is illuminated using a second color (e.g., green) when asecond speech-processing system is being used. The vehicle 110 a mayfurther include one or more buttons 310, dials, switches, triggers, orother such user-input devices. In some embodiments, when the vehicle 110a detects activation of a button 310 and/or touching of the display 302,the microphone 304 captures audio, and the vehicle 110 a sendscorresponding audio data to the system 120. In other embodiments, thevehicle 110 a continually receives audio data captured by theloudspeaker 306 and sends corresponding audio data to the system 120when the vehicle 110 a detects a wakeword in the audio data (asdescribed in greater detail below).

FIGS. 4A, 4B, 4C, and 4D are flow diagrams illustratingspeech-processing system selection according to embodiments of thepresent disclosure. Referring first to FIG. 4A, the system 120 receives(402), from a local device 110, input data, which may be text data,gesture data, or audio data 211. Using the techniques described herein,the local device 110 and/or system 120 determines that the input data211 corresponds to a first speech-processing configuration; thisdetermination may include determining that the audio data 211 includes arepresentation of a wakeword. A speech-processing configuration 292 isselected (404) based on the determination of the configuration; if, forexample, the wakeword is associated with a first speech-processingconfiguration, the first speech processing system configuration (406)the input data. Similarly, if the wakeword is associated with a secondspeech-configuration system, the second speech processing systemprocesses (408) the input data.

The first speech-processing configuration may thus determine (412) firstresponse data, and the second speech-processing configuration maydetermine (414) second response data. The response data may be textdata, audio data, or other data. The system 120 may determine (410)that, though the input data corresponds to a first speech-processingconfiguration, the input data should be further processed by the secondspeech-processing configuration. The system 120 may thus determine (416)an amount of historical interaction between the user and the secondspeech-processing configuration, which may (as described herein) includedetermining an interaction score representing the amount of interaction.The system 120 may then determine (418) an indication of the handoff inaccordance with the interaction score, as described herein, which may beoutput using the local device 110. The system 120 may then direct thesecond speech-processing configuration to process the input data anddetermine the response.

FIG. 4A illustrates a handoff from a first speech-processingconfiguration to a second speech-processing configuration. In someembodiments, a second handoff may occur back from the secondspeech-processing configuration to the first speech-processingconfiguration. This second handoff may be include determination andoutput of a corresponding second indication. The system 120 maydetermine a corresponding interaction score indicating historicalinteraction with the first speech-processing configuration; theindication of the second handoff may be based thereon, as describedherein.

In some embodiments, the system 120 determines an indication from thesecond speech-processing configuration that the first speech-processingconfiguration determined to perform the handoff. This indication may besimilarly determined using the first interaction score or anotherinteraction score.

FIG. 4B illustrates a method for checking user permission. The inputdata is received (420), and the system determines (422) that a handoffto the second speech-processing configuration is required. The system120 may then determine (424) whether permission is required to use thesecond speech-processing configuration (as described herein). If not,the handoff is performed (634). If so, the system 120 determines (426)if the user has granted permission by examining, for example, a userprofile. If not, the system 120 prompts (428) the user for permission.If the user does not accept (430), the system 120 returns an error. Ifthe user account indicates the grant, however, or if the user acquiescesto the prompt the handoff is performed (434).

FIG. 4C illustrates a method of updating a profile of a second personwhen a first speaker interacts with the system 120. The first speakerand second person may have corresponding first and second user profiles.If said user accounts are not present or cannot be determined, thesystem 120 may identify or create first and second speaker profile. Theinput data is received (440), and the system determines (442) that itincludes speech of the first speaker. The system 120 also, as describedherein, determines that the input data indicates (444) a handoff from afirst speech-processing configuration to a second speech-processingconfiguration. The system 120 causes the local device 110 to output(446) an indication of the handoff. A profile associated with the firstspeaker may be updated (450) to reflect this output. As described above,the system 120 may further determine (448) that a second person isproximate the local device 110 by determining that audio data indicatessecond speech (and/or non-speech sounds) of the second person or thatother data, such as biometric data or wireless data, indicates presenceof the second person. The biometric data may be, for example, heartbeatinformation. The wireless data may indicate presence of a wirelessdevice proximate the local device 110. The system 120 may thus alsoupdate (452) a second profile associated with the second person toindicate the output of the indication.

FIG. 4D illustrates a method of determining an interaction scoreaccording to embodiments of the present disclosure. In a first step, thesystem 120 determines (46) that input data corresponds to a profile. Thesystem 120 may, for example, have received data from the local device110 corresponding to a username or user identification information of auser of the local device 110 and identify the profile using the usernameor user identification information. The system 120 may instead or inaddition perform speaker identification using input audio data anddetermine one or more vocal characteristics represented by the audiodata; the profile may include the vocal characteristics and may bedetermined based thereon. As explained above, the input data may furtherindicate presence of one or more other persons proximate the device 110.

The system 120 then determines (462) historical interaction indicated bythe profile. As described herein, this historical interaction mayinclude a number of times that a user of the device 110 has interactedwith the second speech-processing configuration and/or a number of timesthat the user has issued a particular (and/or similar) command to thespeech-processing configuration. The historical information may includeinteractions using the device 110 or other device. The historicalinteraction may include direct uses of the second speech-processingconfiguration (that were not, for example, handed off from the firstspeech-processing configuration). The system 120 may further determine(464) an amount of time that has elapsed since one or more previousinteraction(s), if any.

The interaction determination component 296 may then use the historicalinteraction(s) and/or time elapsed to determine (466) the interactionscore. The interaction score may be determined using an equation; theinteraction determination component 296 may process these inputs usingthe equation to determine the interaction score. For example, a priorinteraction may contribute to the score based on its similarity to aninteraction indicated by the input data as weighted by the inverse ofthe time elapsed (in, e.g., seconds, hours, or days) since the lastinteraction. The interaction determination component 296 may adjust thescore based on positive or negative feedback from the user. Theinteraction determination component 296 may normalize the score to afixed range, such as 0.0-1.0 or 0-5.

FIG. 4E illustrates a set of exemplary indications of a handover from afirst, general-purpose speech-processing configuration to a second,automobile-specific speech-processing configuration when a systemreceives audio data that includes a wakeword corresponding to the firstsystem and a command corresponding to the second system (e.g., “Alexa,roll down my window.). Illustrated are five different interactionscores, in which a higher score indicates a greater interaction. Forexample, a score of 1 corresponds to the indication (represented inresponse data) “SmartCar can do that for you. I will hand you offshortly. Would you first like to hear additional SmartCarcapabilities?”; a score of 2 correspond to the indication “SmartCar cando that for you. I am handing you off”; a score of 3 corresponds to theindication “SmartCar can do that for you”; a score of 4 corresponds tothe indication “OK, here's SmartCar”; and a score of 5 corresponds tothe indication “OK.” In some embodiments, the indication is or includesa nonspeech audio indication of the second speech-processingconfiguration, such as a tone or beep. In other embodiments, theindication is text, video, or other data. The present disclosure is not,limited to any number of scores or types of indications.

FIG. 5 illustrates how NLU processing may be performed on input textdata. Generally, the NLU component 260 (such as the one(s) depicted inFIG. 2) attempts to make a semantic interpretation of text representedin text data. That is, the NLU component 260 determines the meaningbehind the text represented in text data based on the individual words.The NLU component 260 interprets text to derive an intent or a desiredaction of the user as well as the pertinent pieces of information in thetext that allow a device (e.g., local device 110, system 120, skill(s)290, or skill system(s) 225) to complete that action.

The NLU component 260 may process text data including several hypothesesof a single utterance. For example, if the ASR component 250 outputs ASRresults including an N-best list of hypotheses, the NLU component 260may process the text data with respect to all (or a portion of) thetextual interpretations represented therein.

The NLU component 260 may annotate text represented in text data byparsing and/or tagging the text. For example, for the text “tell me theweather for Seattle,” the NLU component 260 may tag “tell me the weatherfor Seattle” as a command (e.g., to output weather information) as wellas tag “Seattle” as a location for the weather information.

The NLU component 260 may include a shortlister component 550. Theshortlister component 550 selects applications that may execute withrespect to text data 610 input to the NLU component (e.g., applicationsthat may execute the command). The shortlister component 550 thus limitsdownstream, more resource intensive NLU processes to being performedwith respect to applications that may execute the command.

Without a shortlister component 550, the NLU component 260 may process agiven hypothesis with respect to every application of the system, eitherin parallel, in series, or using some combination thereof. Byimplementing a shortlister component 550, the NLU component 260 mayprocess a given hypothesis with respect to only the applications thatmay execute the command. This reduces total compute power and latencyattributed to NLU processing.

The NLU component 260 may include one or more recognizers 563 a-n. Eachrecognizer 563 may be associated with a different “function” or “contentsource” (e.g., a different skill 290 or skill). The NLU component 260may determine a function potentially associated with the commandrepresented in text data input thereto in order to determine the properrecognizer 563 to process the hypothesis. The NLU component 260 maydetermine a command represented in text data is potentially associatedwith more than one function. Multiple recognizers 563 may befunctionally linked (e.g., a telephony/communications recognizer and acalendaring recognizer may utilize data from the same contact list).

If the shortlister component 550 determines text corresponding to ahypothesis is potentially associated with multiple skills 290, therecognizers 563 associated with the skills 290 (e.g., the recognizers563 associated with the applications in the subset selected by theshortlister 550) may process the text. The selected recognizers 563 mayprocess the text in parallel, in series, partially in parallel, etc. Forexample, if text corresponding to a hypothesis potentially implicatesboth a communications application and a music application, a recognizerassociated with the communications application may process the text inparallel, or partially in parallel, with a recognizer associated withthe music application processing the text. The output generated by eachrecognizer 563 may be scored, with the overall highest scored outputfrom all recognizers 563 ordinarily being selected to be the correctresult.

If the NLU component 260 determines a command represented in text datais potentially associated with multiple functions, the recognizers 563associated with the functions may each process the text data inparallel. For example, if a command potentially implicates both acommunications function and a music function, a recognizer associatedwith the communications function may process the text data in parallel,or substantially in parallel, with a recognizer associated with themusic function processing the text data. The output generated by eachrecognizer may be scored to indicate the respective recognizersconfidence in its processing of the text data.

The NLU component 260 may communicate with various storages to determinethe potential function(s) associated with a command represented in textdata. The NLU component 260 may communicate with an NLU storage 573,which includes databases of devices (574 a-574 n) identifying functionsassociated with specific devices. For example, the local device 110 maybe associated with functions for music, calendaring, contact lists,device-specific communications, etc. In addition, the NLU component 260may communicate with an entity library 582, which includes databaseentries about specific services on a specific device, either indexed bydevice ID, user ID, or group user ID, or some other indicator.

Each recognizer 563 may include a named entity recognition (NER)component 562. The NER component 562 attempts to identify grammars andlexical information that may be used to construe meaning with respect toa command represented in text data input therein. The NER component 562identifies portions of text represented in text data input into the NLUcomponent 260 that correspond to a named entity that may be recognizableby the system. The NER component 562 (or other component of the NLUcomponent 260) may also determine whether a word refers to an entitythat is not explicitly mentioned in the utterance text, for examplewords such as “him,” “her,” or “it.”

Each recognizer 563, and more specifically each NER component 562, maybe associated with a particular grammar model 576, a particular set ofintents/actions 578, and a particular personalized lexicon 586. Eachgazetteer 584 may include function-indexed lexical informationassociated with a particular user and/or device. For example, gazetteerA (584 a) includes function-indexed lexical information 586 aa to 586an. A user's music function lexical information might include albumtitles, artist names, and song names, for example, whereas a user'scontact-list lexical information might include the names of contacts.Since every user's music collection and contact list is presumablydifferent, this personalized information improves entity resolution.

An NER component 562 may apply grammar models 576 and/or lexicalinformation 586 associated with the function (associated with therecognizer 563 implementing the NER component 562) to determine amention one or more entities in text data input thereto. In this manner,the NER component 562 may identify “slots” (i.e., particular words intext data) that may be needed for later command processing. The NERcomponent 562 may also label each slot with a type of varying levels ofspecificity (e.g., noun, place, city, artist name, song name, etc.).

Each grammar model 576 may include the names of entities (i.e., nouns)commonly found in text about the particular function to which thegrammar model 576 relates, whereas the lexical information 586 ispersonalized to the user(s) and/or the local device 110 from which theinput audio data 211 or input text data originated. For example, agrammar model 576 associated with a shopping function may include adatabase of words commonly used when people discuss shopping.

A process called named entity resolution may link a portion of text toan entity known to the system. To perform this named entity resolution,the NLU component 260 may use gazetteer information (584 a-584 n) storedin an entity library storage 582. The gazetteer information 584 may beused to match text represented in text data with different entities,such as song titles, contact names, etc. Gazetteers may be linked tousers (e.g., a particular gazetteer may be associated with a specificuser's music collection), may be linked to certain function categories(e.g., shopping, music, video, communications, etc.), or may beorganized in a variety of other ways.

Each recognizer 563 may also include an intent classification (IC)component 564. The IC component 564 parses text data to determine anintent(s) of the function associated with the recognizer 563 thatpotentially corresponds to the text data. An intent corresponds to anaction to be performed that is responsive to the command represented bythe text data. The IC component 564 may communicate with a database 578of words linked to intents. For example, a music intent database maylink words and phrases such as “quiet,” “volume off,” and “mute” to a“mute” intent. The IC component 564 identifies potential intents bycomparing words in the text data to the words and phrases in an intentsdatabase 578 associated with the function that is associated with therecognizer 563 implementing the IC component 564.

The intents identifiable by a specific IC component 564 may be linked tofunction-specific (i.e., the function associated with the recognizer 563implementing the IC component 564) grammar model 576 with “slots” to befilled. Each slot of a grammar model 576 may correspond to a portion ofthe text data that the system believes corresponds to an entity. Forexample, a grammar model 576 corresponding to a <PlayMusic> intent maycorrespond to text data sentence structures such as “Play {ArtistName},” “Play {Album Name},” “Play {Song name},” “Play {Song name} by{Artist Name},” etc. However, to make resolution more flexible, grammarmodels 576 may not be structured as sentences, but rather based onassociating slots with grammatical tags.

For example, an NER component 562 may parse text data to identify wordsas subject, object, verb, preposition, etc. based on grammar rulesand/or models prior to recognizing named entities in the text data. AnIC component 564 (implemented by the same recognizer 563 as the NERcomponent 562) may use the identified verb to identify an intent. TheNER component 562 may then determine a grammar model 576 associated withthe identified intent. For example, a grammar model 576 for an intentcorresponding to <PlayMusic> may specify a list of slots applicable toplay the identified object and any object modifier (e.g., aprepositional phrase), such as {Artist Name}, {Album Name}, {Song name},etc. The NER component 562 may then search corresponding fields in alexicon 586 associated with the function associated with the recognizer563 implementing the NER component 562 and may match words and phrasesin the text data the NER component 562 previously tagged as agrammatical object or object modifier with those identified in thelexicon 586.

The NER component 562 may perform semantic tagging, which is thelabeling of a word or combination of words according to theirtype/semantic meaning. The NER component 562 may parse text data usingheuristic grammar rules, or a model may be constructed using techniquessuch as hidden Markov models, maximum entropy models, log linear models,conditional random fields (CRF), and the like. For example, an NERcomponent 562 implemented by a music function recognizer 563 may parseand tag text corresponding to “play mother's little helper by therolling stones” as {Verb}: “Play,” {Object}: “mother's little helper,”{Object Preposition}: “by,” and {Object Modifier}: “the rolling stones.”The NER component 562 may identify “Play” as a verb based on a worddatabase associated with the music function, which an IC component 564(which may also implemented by the music function recognizer 563) maydetermine that the word corresponds to a <PlayMusic> intent. At thisstage, no determination may have been made as to the meaning of“mother's little helper” and “the rolling stones,” but based on grammarrules and models, the NER component 562 may have determined that thetext of these phrases relates to the grammatical object (i.e., entity)of the text data.

The frameworks linked to the intent may then be used to determine whatdatabase fields may be searched to determine the meaning of thesephrases, such as searching a user's gazetteer 584 for similarity withthe framework slots. For example, a framework for a <PlayMusic> intentmight indicate to attempt to resolve the identified object based {ArtistName}, {Album Name}, and {Song name}, and another framework for the sameintent might indicate to attempt to resolve the object modifier based on{Artist Name}, and resolve the object based on {Album Name} and {SongName} linked to the identified {Artist Name}. If the search of thegazetteer 584 does not resolve a slot/field using gazetteer information,the NER component 562 may search, in the knowledge base 572, thedatabase of generic words associated with the function. For example, ifthe text data includes text corresponding to “play songs by the rollingstones,” after failing to determine an album name or song name called“songs” by “the rolling stones,” the NER component 562 may search thefunction's vocabulary for the word “songs.” In the some embodiments,generic words may be checked before the gazetteer information, or bothmay be tried, potentially producing two different results.

The NLU component 260 may tag text to attribute meaning to the text. Forexample, the NLU component 260 may tag “play mother's little helper bythe rolling stones” as {intent}: <PlayMusic>, {artist name}: rollingstones, {media type}: SONG, and {song title}: mother's little helper. Inanother example, the NLU component 260 may tag “play songs by therolling stones” as {intent}: <PlayMusic>, {artist name}: rolling stones,and {media type}: SONG.

The shortlister component 550 may receive text data 610 output from theASR component 250 (as illustrated in FIG. 6). The ASR component 250 mayembed the text data 610 into a form processable by a trained model(s)using sentence-embedding techniques. Sentence embedding may include, inthe text data 610, text in a structure that enables the trained modelsof the shortlister component 550 to operate on the text. For example, anembedding of the text data 610 may be a vector representation of thetext data.

The shortlister component 550 may make binary determinations (e.g., yesor no determinations) regarding which skill(s) 290 relate to the textdata 610. The shortlister component 550 may make such determinationsusing the one or more trained models described herein above. If theshortlister component 550 implements a single trained model for eachskill 290, the shortlister component 550 may simply run the models thatare associated with enabled applications as indicated in a profileassociated with the local device 110 and/or user that originated thecommand.

The shortlister component 550 may generate N-best list data representingapplications that may execute with respect to the command represented inthe text data 610. The size of the N-best list represented in the N-bestlist data is configurable. In an example, the N-best list data mayindicate every application of the system as well as contain anindication, for each application, regarding whether the application islikely capable to execute the command represented in the text data 610.In another example, instead of indicating every application of thesystem, the N-best list data may only indicate all of the applicationsthat are likely to be able to execute the command represented in thetext data 610. In yet another example, the shortlister component 550 mayimplement thresholding such that the N-best list data may indicate nomore than a maximum number of applications that may execute the commandrepresented in the text data 610. In an example, the threshold number ofapplications that may be represented in the N-best list data is ten(10). In another example, the applications included in the N-best listdata may be limited by a threshold a score, where only applicationsindicating a likelihood to handle the command is above a certain score(as determined by processing the text data 610 by the shortlistercomponent 550 relative to such applications).

The NLU component 260 may compile data, output by each of therecognizers 563 that processed the text data input to the NLU component260, into a single N-best list, and may send N-best list data 640(representing the N-best list) to a pruning component 650 (asillustrated in FIG. 6). Each entry in the N-best list data 640 maycorrespond to tagged text output by a different recognizer 563. Eachentry in the N-best list data 640 may be associated with a respectivescore indicating the tagged text corresponds to the function associatedwith the recognizer 563 from which the tagged text was output. Forexample, the N-best list data 640 may be represented as:

-   -   [0.95] Intent: <PlayMusic> Source: Alexa, SmartCar    -   [0.70] Intent: <RollWindow> Source: SmartCar    -   [0.01] Intent: <Navigate> Source: Alexa, SmartCar    -   [0.01] Intent: <PlayVideo> Source: Alexa

The pruning component 650 creates a new, shorter N-best list (i.e.,represented in N-best list data 660 discussed below) based on the N-bestlist data 640. The pruning component 650 may sort the tagged textrepresented in the N-best list data 640 according to their respectivescores.

The pruning component 650 may perform score thresholding with respect tothe N-best list data 640. For example, the pruning component 650 mayselect entries represented in the N-best list data 640 associated with ascore satisfying (e.g., meeting and/or exceeding) a score threshold. Thepruning component 650 may also or alternatively perform number of entrythresholding. For example, the pruning component 650 may select the topscoring entry(ies) associated with each different category of function(e.g., music, shopping, communications, etc.) represented in the N-bestlist data 640, with the new N-best list data 660 including a totalnumber of entries meeting or falling below a threshold number ofentries. The purpose of the pruning component 650 is to create a newlist of top scoring entries so that downstream, more resource intensiveprocesses may only operate on the tagged text entries that most likelycorrespond to the command input to the system.

The NLU component 260 may also include a light slot filler component652. The light slot filler component 652 can take text from slotsrepresented in the tagged text entry or entries output by the pruningcomponent 650 and alter it to make the text more easily processed bydownstream components. The light slot filler component 652 may performlow latency operations that do not involve heavy operations such asreference to a knowledge base. The purpose of the light slot fillercomponent 652 is to replace words with other words or values that may bemore easily understood by downstream components. For example, if atagged text entry includes the word “tomorrow,” the light slot fillercomponent 652 may replace the word “tomorrow” with an actual date forpurposes of downstream processing. Similarly, the light slot fillercomponent 652 may replace the word “CD” with “album” or the words“compact disc.” The replaced words are then included in the N-best listdata 660.

The NLU component 260 sends the N-best list data 660 to an entityresolution component 670. The entity resolution component 670 can applyrules or other instructions to standardize labels or tokens fromprevious stages into an intent/slot representation. The precisetransformation may depend on the function (e.g., for a travel function,the entity resolution component 670 may transform a text mention of“Boston airport” to the standard BOS three-letter code referring to theairport). The entity resolution component 670 can refer to an authoritysource (e.g., a knowledge base) that is used to specifically identifythe precise entity referred to in each slot of each tagged text entryrepresented in the N-best list data 660. Specific intent/slotcombinations may also be tied to a particular source, which may then beused to resolve the text. In the example “play songs by the stones,” theentity resolution component 670 may reference a personal music catalog,Amazon Music account, user profile (described herein), or the like. Theentity resolution component 670 may output data including an alteredN-best list that is based on the N-best list represented in the N-bestlist data 660, but also includes more detailed information (e.g., entityIDs) about the specific entities mentioned in the slots and/or moredetailed slot data that can eventually be used by a function. The NLUcomponent 260 may include multiple entity resolution components 670 andeach entity resolution component 670 may be specific to one or morefunctions.

The entity resolution component 670 may not be successful in resolvingevery entity and filling every slot represented in the N-best list data660. This may result in the entity resolution component 670 outputtingincomplete results. The NLU component 260 may include a final rankercomponent 690, which may consider such errors when determining how torank the tagged text entries for potential execution. For example, if abook function recognizer 563 outputs a tagged text entry including a<ReadBook> intent flag, but the entity resolution component 670 cannotfind a book with a title matching the text of the item, the final rankercomponent 690 may re-score that particular tagged text entry to be givena lower score. The final ranker component 690 may also assign aparticular confidence to each tagged text entry input therein. Theconfidence score of a particular tagged text entry may be affected bywhether the tagged text entry has unfilled slots. For example, if atagged text entry associated with a first function includes slots thatare all filled/resolved, that tagged text entry may be associated with ahigher confidence than another tagged text entry including at least someslots that are unfilled/unresolved.

The final ranker component 690 may apply re-scoring, biasing, or othertechniques to obtain the most preferred tagged and resolved text entry.To do so, the final ranker component 690 may consider not only the dataoutput by the entity resolution component 670, but may also considerother data 691. The other data 691 may include a variety of information.For example, the other data 691 may include function rating orpopularity data. For example, if one function has a particularly highrating, the final ranker component 690 may increase the score of atagged text entry or entries associated with or otherwise invoking thatparticular function. The other data 691 may also include informationabout functions that have been specifically enabled by the user. Forexample, the final ranker component 690 may assign higher scores totagged text entries associated with or otherwise invoking enabledfunctions than tagged text entries associated with or otherwise invokingnon-enabled functions. User history may also be considered, such as ifthe user regularly uses a particular function or does so at particulartimes of day. Date, time, location, weather, type of local device 110,user ID, context, and other information may also be considered. Forexample, the final ranker component 690 may consider when any particularfunctions are currently active (e.g., music being played, a game beingplayed, etc.). Following final ranking, the NLU component 260 may outputNLU output data 685 to the orchestrator component 240. The NLU outputdata 685 may include various entries, with each entry representing anNLU processing confidence score, an intent, slot data, and a potentialskill or skill that may operating with respect to the respective entry'sdata.

Following preliminary ranking, the NLU component 260 may output NLUresults data 685. The NLU component 260 may send the NLU results data685 to the orchestrator component 240. The NLU results data 685 mayinclude first NLU results data 685 a including tagged text associatedwith a first skill, second NLU results data 685 b including tagged textassociated with a second skill, etc. The NLU results data 685 mayinclude tagged text data corresponding to the top scoring tagged textentries as determined by the preliminary ranker component 690.

The data 685 output from the NLU component 260 may include an N-bestlist of NLU results, where each item in the N-best list may correspondto a particular recognizer 563 and corresponding skill 290. Thus, forexample, first NLU results of the N-best list may be associated with afirst skill 290 a, second NLU results of the N-best list may beassociated with a second skill 290 b, third NLU results of the N-bestlist may be associated with a third skill 290 c, etc. Moreover, thefirst NLU results may correspond to text tagged to attribute meaningthat enables the first skill 290 a to execute with respect to the firstNLU results, the second NLU results may correspond to text tagged toattribute meaning that enables the second skill 290 b to execute withrespect to the second NLU results, the third NLU results may correspondto text tagged to attribute meaning that enables the third skill 290 cto execute with respect to the third NLU results, etc. The data 685 mayalso include scores corresponding to each item in the N-best list.Alternatively, the NLU result data 685 output to a particular skill 290may include NER and IC data output by the particular skill's recognizer563 while the NLU result data 685 output to the orchestrator component240 may include only a portion of the NLU result data 685, for examplethe scores corresponding to certain skills.

The system may be configured with thousands, tens of thousands, etc.skills 290. The orchestrator component 240 enables the system to betterdetermine the best skill 290 to execute the command input to the system.For example, first NLU results may correspond or substantiallycorrespond to second NLU results, even though the first NLU results areoperated on by a first skill 290 a and the second NLU results areoperated on by a second skill 290 b. The first NLU results may beassociated with a first confidence score indicating the system'sconfidence with respect to NLU processing performed to generate thefirst NLU results. Moreover, the second NLU results may be associatedwith a second confidence score indicating the system's confidence withrespect to NLU processing performed to generate the second NLU results.The first confidence score may be similar or identical to the secondconfidence score since the first NLU results correspond or substantiallycorrespond to the second NLU results. The first confidence score and/orthe second confidence score may be a numeric value (e.g., from 0.0 to1.0). Alternatively, the first confidence score and/or the secondconfidence score may be a binned value (e.g., low, medium, high).

The orchestrator component 240 may solicit the first skill 290 a and thesecond skill 290 b to provide potential result data based on the firstNLU results and the second NLU results, respectively. For example, theorchestrator component 240 may send the first NLU results to the firstskill 290 a along with a request for the first skill 290 a to at leastpartially execute a command with respect to the first NLU results. Theorchestrator component 240 may also send the second NLU results to thesecond skill 290 b along with a request for the second skill 290 b to atleast partially execute a command with respect to the first NLU results.The orchestrator component 240 receives, from the first skill 290 a,first result data generated from the first skill's execution withrespect to the first NLU results. The orchestrator component 240 alsoreceives, from the second skill 290 b, second results data generatedfrom the second skill's execution with respect to the second NLUresults.

The result data 685 may include various components. For example, theresult data 685 may include content (e.g., audio data, text data, and/orvideo data) to be output to a user. The result data 685 may also includea unique identifier (ID) used by the remote system 120 and/or the skillserver(s) 225 to locate the data to be output to a user. The result data685 may also include an instruction. For example, if the commandcorresponds to “turn on the light,” the result data 685 may include aninstruction causing the system to turn on a light associated with aprofile of the local device 110 and/or user.

The orchestrator component 240 may, prior to sending the NLU resultsdata 685 to the orchestrator component 240, associate intents in the NLUresults data 685 with skills 290. For example, if the NLU results data685 includes a <PlayMusic> intent, the orchestrator component 240 mayassociate the NLU results data 685 with one or more skills 290 that canexecute the <PlayMusic> intent. Thus, the orchestrator component 240 maysend the NLU results data 685 paired with skills 290 to the orchestratorcomponent 240. In response to input text data corresponding to “whatshould I do for dinner today,” the orchestrator component 240 maygenerates pairs of skills 290 with associated intents corresponding to:

-   -   Skill 1/<Roll Down Window>    -   Skill 2/<Start Navigation>    -   Skill 3/<Play Music>

A system that does not use the orchestrator component 240 as describedabove may instead select the highest scored preliminary ranked NLUresults data 685 associated with a single skill. The system may send theNLU results data 685 to the skill 290 along with a request for outputdata. In some situations, the skill 290 may not be able to provide thesystem with output data. This results in the system indicating to theuser that the command could not be processed even though another skillassociated with lower ranked NLU results data 685 could have providedoutput data responsive to the command.

Components of a system that may be used to perform unit selection,parametric TTS processing, and/or model-based audio synthesis are shownin FIG. 7A. As shown in FIG. 7A, the TTS component/processor 780 mayinclude a TTS front end 716, a speech synthesis engine 718, TTS unitstorage 772, TTS parametric storage 780, and a TTS back end 734. The TTSunit storage 772 may include, among other things, voice inventories 778a-288 n that may include pre-recorded audio segments (called units) tobe used by the unit selection engine 730 when performing unit selectionsynthesis as described below. The TTS parametric storage 780 mayinclude, among other things, parametric settings 768 a-268 n that may beused by the parametric synthesis engine 732 when performing parametricsynthesis as described below. A particular set of parametric settings768 may correspond to a particular voice profile (e.g., whisperedspeech, excited speech, etc.).

In various embodiments of the present disclosure, model-based synthesisof audio data may be performed using by a speech model 722 and a TTSfront-end 716. The TTS front-end 716 may be the same as front ends usedin traditional unit selection or parametric systems. In otherembodiments, some or all of the components of the TTS front end 716 arebased on other trained models. The present disclosure is not, however,limited to any particular type of TTS front end 716. The speech model722 may be used to synthesize speech without requiring the TTS unitstorage 772 or the TTS parametric storage 780, as described in greaterdetail below.

The TTS front end 716 transforms input text data 710 (from, for example,an application, user, device, or other text source) into a symboliclinguistic representation, which may include linguistic context featuressuch as phoneme data, punctuation data, syllable-level features,word-level features, and/or emotion, speaker, accent, or other featuresfor processing by the speech synthesis engine 718. The syllable-levelfeatures may include syllable emphasis, syllable speech rate, syllableinflection, or other such syllable-level features; the word-levelfeatures may include word emphasis, word speech rate, word inflection,or other such word-level features. The emotion features may include datacorresponding to an emotion associated with the input text data 710,such as surprise, anger, or fear. The speaker features may include datacorresponding to a type of speaker, such as sex, age, or profession. Theaccent features may include data corresponding to an accent associatedwith the speaker, such as Southern, Boston, English, French, or othersuch accent.

The TTS front end 716 may also process other input data 715, such astext tags or text metadata, that may indicate, for example, how specificwords should be pronounced, for example by indicating the desired outputspeech quality in tags formatted according to the speech synthesismarkup language (SSML) or in some other form. For example, a first texttag may be included with text marking the beginning of when text shouldbe whispered (e.g., <begin whisper>) and a second tag may be includedwith text marking the end of when text should be whispered (e.g., <endwhisper>). The tags may be included in the input text data 710 and/orthe text for a TTS request may be accompanied by separate metadataindicating what text should be whispered (or have some other indicatedaudio characteristic). The speech synthesis engine 718 may compare theannotated phonetic units models and information stored in the TTS unitstorage 772 and/or TTS parametric storage 780 for converting the inputtext into speech. The TTS front end 716 and speech synthesis engine 718may include their own controller(s)/processor(s) and memory or they mayuse the controller/processor and memory of the server 120, device 110,or other device, for example. Similarly, the instructions for operatingthe TTS front end 716 and speech synthesis engine 718 may be locatedwithin the TTS component 780, within the memory and/or storage of theserver 120, device 110, or within an external device.

Text data 710 input into the TTS component 780 may be sent to the TTSfront end 716 for processing. The front-end may include components forperforming text normalization, linguistic analysis, linguistic prosodygeneration, or other such components. During text normalization, the TTSfront end 716 may first process the text input and generate standardtext, converting such things as numbers, abbreviations (such as Apt.,St., etc.), symbols ($, %, etc.) into the equivalent of written outwords.

During linguistic analysis, the TTS front end 716 may analyze thelanguage in the normalized text to generate a sequence of phonetic unitscorresponding to the input text. This process may be referred to asgrapheme-to-phoneme conversion. Phonetic units include symbolicrepresentations of sound units to be eventually combined and output bythe system as speech. Various sound units may be used for dividing textfor purposes of speech synthesis. The TTS component 780 may processspeech based on phonemes (individual sounds), half-phonemes, di-phones(the last half of one phoneme coupled with the first half of theadjacent phoneme), bi-phones (two consecutive phonemes), syllables,words, phrases, sentences, or other units. Each word may be mapped toone or more phonetic units. Such mapping may be performed using alanguage dictionary stored by the system, for example in the TTS storagecomponent 772. The linguistic analysis performed by the TTS front end716 may also identify different grammatical components such as prefixes,suffixes, phrases, punctuation, syntactic boundaries, or the like. Suchgrammatical components may be used by the TTS component 780 to craft anatural-sounding audio waveform output. The language dictionary may alsoinclude letter-to-sound rules and other tools that may be used topronounce previously unidentified words or letter combinations that maybe encountered by the TTS component 780. Generally, the more informationincluded in the language dictionary, the higher quality the speechoutput.

Based on the linguistic analysis the TTS front end 716 may then performlinguistic prosody generation where the phonetic units are annotatedwith desired prosodic characteristics, also called acoustic features,which indicate how the desired phonetic units are to be pronounced inthe eventual output speech. During this stage the TTS front end 716 mayconsider and incorporate any prosodic annotations that accompanied thetext input to the TTS component 780. Such acoustic features may includesyllable-level features, word-level features, emotion, speaker, accent,language, pitch, energy, duration, and the like. Application of acousticfeatures may be based on prosodic models available to the TTS component780. Such prosodic models indicate how specific phonetic units are to bepronounced in certain circumstances. A prosodic model may consider, forexample, a phoneme's position in a syllable, a syllable's position in aword, a word's position in a sentence or phrase, neighboring phoneticunits, etc. As with the language dictionary, prosodic model with moreinformation may result in higher quality speech output than prosodicmodels with less information. Further, a prosodic model and/or phoneticunits may be used to indicate particular speech qualities of the speechto be synthesized, where those speech qualities may match the speechqualities of input speech (for example, the phonetic units may indicateprosodic characteristics to make the ultimately synthesized speech soundlike a whisper based on the input speech being whispered).

The output of the TTS front end 716, which may be referred to as asymbolic linguistic representation, may include a sequence of phoneticunits annotated with prosodic characteristics. This symbolic linguisticrepresentation may be sent to the speech synthesis engine 718, which mayalso be known as a synthesizer, for conversion into an audio waveform ofspeech for output to an audio output device and eventually to a user.The speech synthesis engine 718 may be configured to convert the inputtext into high-quality natural-sounding speech in an efficient manner.Such high-quality speech may be configured to sound as much like a humanspeaker as possible, or may be configured to be understandable to alistener without attempts to mimic a precise human voice.

The speech synthesis engine 718 may perform speech synthesis using oneor more different methods. In one method of synthesis called unitselection, described further below, a unit selection engine 730 matchesthe symbolic linguistic representation created by the TTS front end 716against a database of recorded speech, such as a database (e.g., TTSunit storage 772) storing information regarding one or more voicecorpuses (e.g., voice inventories 778 a-n). Each voice inventory maycorrespond to various segments of audio that was recorded by a speakinghuman, such as a voice actor, where the segments are stored in anindividual inventory 778 as acoustic units (e.g., phonemes, diphones,etc.). Each stored unit of audio may also be associated with an indexlisting various acoustic properties or other descriptive informationabout the unit. Each unit includes an audio waveform corresponding witha phonetic unit, such as a short .wav file of the specific sound, alongwith a description of various features associated with the audiowaveform. For example, an index entry for a particular unit may includeinformation such as a particular unit's pitch, energy, duration,harmonics, center frequency, where the phonetic unit appears in a word,sentence, or phrase, the neighboring phonetic units, or the like. Theunit selection engine 730 may then use the information about each unitto select units to be joined together to form the speech output.

The unit selection engine 730 matches the symbolic linguisticrepresentation against information about the spoken audio units in thedatabase. The unit database may include multiple examples of phoneticunits to provide the system with many different options forconcatenating units into speech. Matching units which are determined tohave the desired acoustic qualities to create the desired output audioare selected and concatenated together (for example by a synthesiscomponent 720) to form output audio data X790 representing synthesizedspeech. Using all the information in the unit database, a unit selectionengine 730 may match units to the input text to select units that canform a natural sounding waveform. One benefit of unit selection is that,depending on the size of the database, a natural sounding speech outputmay be generated. As described above, the larger the unit database ofthe voice corpus, the more likely the system will be able to constructnatural sounding speech.

In another method of synthesis—called parametric synthesis—parameterssuch as frequency, volume, noise, are varied by a parametric synthesisengine 732, digital signal processor or other audio generation device tocreate an artificial speech waveform output. Parametric synthesis uses acomputerized voice generator, sometimes called a vocoder. Parametricsynthesis may use an acoustic model and various statistical techniquesto match a symbolic linguistic representation with desired output speechparameters. Using parametric synthesis, a computing system (for example,a synthesis component 720) can generate audio waveforms having thedesired acoustic properties. Parametric synthesis may include theability to be accurate at high processing speeds, as well as the abilityto process speech without large databases associated with unitselection, but also may produce an output speech quality that may notmatch that of unit selection. Unit selection and parametric techniquesmay be performed individually or combined together and/or combined withother synthesis techniques to produce speech audio output.

The TTS component 780 may be configured to perform TTS processing inmultiple languages. For each language, the TTS component 780 may includespecially configured data, instructions and/or components to synthesizespeech in the desired language(s). To improve performance, the TTScomponent 780 may revise/update the contents of the TTS storage 780based on feedback of the results of TTS processing, thus enabling theTTS component 780 to improve speech recognition.

The TTS storage component 780 may be customized for an individual userbased on his/her individualized desired speech output. In particular,the speech unit stored in a unit database may be taken from input audiodata of the user speaking. For example, to create the customized speechoutput of the system, the system may be configured with multiple voiceinventories 778 a-278 n, where each unit database is configured with adifferent “voice” to match desired speech qualities. Such voiceinventories may also be linked to user accounts. The voice selected bythe TTS component 780 to synthesize the speech. For example, one voicecorpus may be stored to be used to synthesize whispered speech (orspeech approximating whispered speech), another may be stored to be usedto synthesize excited speech (or speech approximating excited speech),and so on. To create the different voice corpuses a multitude of TTStraining utterances may be spoken by an individual (such as a voiceactor) and recorded by the system. The audio associated with the TTStraining utterances may then be split into small audio segments andstored as part of a voice corpus. The individual speaking the TTStraining utterances may speak in different voice qualities to create thecustomized voice corpuses, for example the individual may whisper thetraining utterances, say them in an excited voice, and so on. Thus theaudio of each customized voice corpus may match the respective desiredspeech quality. The customized voice inventory 778 may then be usedduring runtime to perform unit selection to synthesize speech having aspeech quality corresponding to the input speech quality.

Additionally, parametric synthesis may be used to synthesize speech withthe desired speech quality. For parametric synthesis, parametricfeatures may be configured that match the desired speech quality. Ifsimulated excited speech was desired, parametric features may indicatean increased speech rate and/or pitch for the resulting speech. Manyother examples are possible. The desired parametric features forparticular speech qualities may be stored in a “voice” profile (e.g.,parametric settings 768) and used for speech synthesis when the specificspeech quality is desired. Customized voices may be created based onmultiple desired speech qualities combined (for either unit selection orparametric synthesis). For example, one voice may be “shouted” whileanother voice may be “shouted and emphasized.” Many such combinationsare possible.

Unit selection speech synthesis may be performed as follows. Unitselection includes a two-step process. First a unit selection engine 730determines what speech units to use and then it combines them so thatthe particular combined units match the desired phonemes and acousticfeatures and create the desired speech output. Units may be selectedbased on a cost function which represents how well particular units fitthe speech segments to be synthesized. The cost function may represent acombination of different costs representing different aspects of howwell a particular speech unit may work for a particular speech segment.For example, a target cost indicates how well an individual given speechunit matches the features of a desired speech output (e.g., pitch,prosody, etc.). A join cost represents how well a particular speech unitmatches an adjacent speech unit (e.g., a speech unit appearing directlybefore or directly after the particular speech unit) for purposes ofconcatenating the speech units together in the eventual synthesizedspeech. The overall cost function is a combination of target cost, joincost, and other costs that may be determined by the unit selectionengine 730. As part of unit selection, the unit selection engine 730chooses the speech unit with the lowest overall combined cost. Forexample, a speech unit with a very low target cost may not necessarilybe selected if its join cost is high.

The system may be configured with one or more voice corpuses for unitselection. Each voice corpus may include a speech unit database. Thespeech unit database may be stored in TTS unit storage 772 or in anotherstorage component. For example, different unit selection databases maybe stored in TTS unit storage 772. Each speech unit database (e.g.,voice inventory) includes recorded speech utterances with theutterances' corresponding text aligned to the utterances. A speech unitdatabase may include many hours of recorded speech (in the form of audiowaveforms, feature vectors, or other formats), which may occupy asignificant amount of storage. The unit samples in the speech unitdatabase may be classified in a variety of ways including by phoneticunit (phoneme, diphone, word, etc.), linguistic prosodic label, acousticfeature sequence, speaker identity, etc. The sample utterances may beused to create mathematical models corresponding to desired audio outputfor particular speech units. When matching a symbolic linguisticrepresentation the speech synthesis engine 718 may attempt to select aunit in the speech unit database that most closely matches the inputtext (including both phonetic units and prosodic annotations). Generallythe larger the voice corpus/speech unit database the better the speechsynthesis may be achieved by virtue of the greater number of unitsamples that may be selected to form the precise desired speech output.

Vocoder-based parametric speech synthesis may be performed as follows. ATTS component 780 may include an acoustic model, or other models, whichmay convert a symbolic linguistic representation into a syntheticacoustic waveform of the text input based on audio signal manipulation.The acoustic model includes rules which may be used by the parametricsynthesis engine 732 to assign specific audio waveform parameters toinput phonetic units and/or prosodic annotations. The rules may be usedto calculate a score representing a likelihood that a particular audiooutput parameter(s) (such as frequency, volume, etc.) corresponds to theportion of the input symbolic linguistic representation from the TTSfront end 716.

The parametric synthesis engine 732 may use a number of techniques tomatch speech to be synthesized with input phonetic units and/or prosodicannotations. One common technique is using Hidden Markov Models (HMMs).HMMs may be used to determine probabilities that audio output shouldmatch textual input. HMMs may be used to translate from parameters fromthe linguistic and acoustic space to the parameters to be used by avocoder (the digital voice encoder) to artificially synthesize thedesired speech. Using HMMs, a number of states are presented, in whichthe states together represent one or more potential acoustic parametersto be output to the vocoder and each state is associated with a model,such as a Gaussian mixture model. Transitions between states may alsohave an associated probability, representing a likelihood that a currentstate may be reached from a previous state. Sounds to be output may berepresented as paths between states of the HMM and multiple paths mayrepresent multiple possible audio matches for the same input text. Eachportion of text may be represented by multiple potential statescorresponding to different known pronunciations of phonemes and theirparts (such as the phoneme identity, stress, accent, position, etc.). Aninitial determination of a probability of a potential phoneme may beassociated with one state. As new text is processed by the speechsynthesis engine 718, the state may change or stay the same, based onthe processing of the new text. For example, the pronunciation of apreviously processed word might change based on later processed words. AViterbi algorithm may be used to find the most likely sequence of statesbased on the processed text. The HMMs may generate speech inparameterized form including parameters such as fundamental frequency(f0), noise envelope, spectral envelope, etc. that are translated by avocoder into audio segments. The output parameters may be configured forparticular vocoders such as a STRAIGHT vocoder, TANDEM-STRAIGHT vocoder,WORLD vocoder, HNM (harmonic plus noise) based vocoders, CELP(code-excited linear prediction) vocoders, GlottHMM vocoders, HSM(harmonic/stochastic model) vocoders, or others.

In addition to calculating potential states for one audio waveform as apotential match to a phonetic unit, the parametric synthesis engine 732may also calculate potential states for other potential audio outputs(such as various ways of pronouncing a particular phoneme or diphone) aspotential acoustic matches for the acoustic unit. In this mannermultiple states and state transition probabilities may be calculated.

The probable states and probable state transitions calculated by theparametric synthesis engine 732 may lead to a number of potential audiooutput sequences. Based on the acoustic model and other potentialmodels, the potential audio output sequences may be scored according toa confidence level of the parametric synthesis engine 732. The highestscoring audio output sequence, including a stream of parameters to besynthesized, may be chosen and digital signal processing may beperformed by a vocoder or similar component to create an audio outputincluding synthesized speech waveforms corresponding to the parametersof the highest scoring audio output sequence and, if the proper sequencewas selected, also corresponding to the input text. The differentparametric settings 768, which may represent acoustic settings matchinga particular parametric “voice”, may be used by the synthesis component722 to ultimately create the output audio data 790.

When performing unit selection, after a unit is selected by the unitselection engine 730, the audio data corresponding to the unit may bepassed to the synthesis component 720. The synthesis component 720 maythen process the audio data of the unit to create modified audio datawhere the modified audio data reflects a desired audio quality. Thesynthesis component 720 may store a variety of operations that canconvert unit audio data into modified audio data where differentoperations may be performed based on the desired audio effect (e.g.,whispering, shouting, etc.).

As an example, input text may be received along with metadata, such asSSML tags, indicating that a selected portion of the input text shouldbe whispered when output by the TTS module 780. For each unit thatcorresponds to the selected portion, the synthesis component 720 mayprocess the audio data for that unit to create a modified unit audiodata. The modified unit audio data may then be concatenated to form theoutput audio data X790. The modified unit audio data may also beconcatenated with non-modified audio data depending on when the desiredwhispered speech starts and/or ends. While the modified audio data maybe sufficient to imbue the output audio data with the desired audioqualities, other factors may also impact the ultimate output of audiosuch as playback speed, background effects, or the like, that may beoutside the control of the TTS module 780. In that case, other outputdata 785 may be output along with the output audio data X790 so that anultimate playback device (e.g., device 110) receives instructions forplayback that can assist in creating the desired output audio. Thus, theother output data 785 may include instructions or other data indicatingplayback device settings (such as volume, playback rate, etc.) or otherdata indicating how output audio data including synthesized speechshould be output. For example, for whispered speech, the output audiodata X790 may include other output data 785 that may include a prosodytag or other indicator that instructs the device 110 to slow down theplayback of the output audio data X790, thus making the ultimate audiosound more like whispered speech, which is typically slower than normalspeech. In another example, the other output data 785 may include avolume tag that instructs the device 110 to output the speech at avolume level less than a current volume setting of the device 110, thusimproving the quiet whisper effect.

FIG. 7B illustrates an embodiment of the speech model 722. The speechmodel may include an encoder 750, attention mechanism 752, and a decoder754. This arrangement of components may be referred to as asequence-to-sequence model. The encoder 750 and/or decoder 754 may beneural networks having one or more layers. These layers may consist ofone or more nodes; each node may receive input data or the output of anode from a previous layer and process that data in accordance with oneor more model weights. For example, a node may multiply a value of aninput with a model weight to produce an output. The neural networks maybe deep neural networks (DNNs), convolutional neural networks (CNNs),and/or recurrent neural networks (RNNs). The neural networks may betrained using training data, such as recordings of utterances andcorresponding text.

As descried herein, the interaction determination component 296determines an interaction score 760. The encoder 750 may receive thisinteraction score 760, as well as input text data 710 corresponding toinput data from the device 110 and/or the NLU result data 685. Theencoder 750 may encode this information into a context vector, which isinput to the decoder 754. Optionally, an attention mechanism 752 mayreceive this context vector as well as outputs of other nodes of theencoder 750 and weight (e.g., “attend”) different outputs of the encoder750 differently. The decoder 754 may then generate output audio data 790(which may include the response data) using the context vector and/oroutput of the attention mechanism 752.

As illustrated in FIG. 8, the user-recognition component 295 may includeone or more subcomponents including a vision component 808, an audiocomponent 810, a biometric component 812, a radio-frequency (RF)component 814, a machine-learning (ML) component 816, and a recognitionconfidence component 818. In some instances, the user-recognitioncomponent 295 may monitor data and determinations from one or moresubcomponents to determine an identity of one or more users associatedwith data input to the system. The user-recognition component 295 mayoutput user-recognition data 895, which may include a user identifierassociated with a user the system believes is originating data input tothe system. The user-recognition data 895 may be used to informprocesses performed by the orchestrator 240 (or a subcomponent thereof)as described below.

The vision component 808 may receive data from one or more sensorscapable of providing images (e.g., cameras) or sensors indicating motion(e.g., motion sensors). The vision component 808 can perform facialrecognition or image analysis to determine an identity of a user and toassociate that identity with a user profile associated with the user. Insome instances, when a user is facing a camera, the vision component 808may perform facial recognition and identify the user with a high degreeof confidence. In other instances, the vision component 808 may have alow degree of confidence of an identity of a user, and theuser-recognition component 295 may utilize determinations fromadditional components to determine an identity of a user. The visioncomponent 808 can be used in conjunction with other components todetermine an identity of a user. For example, the user-recognitioncomponent 295 may use data from the vision component 808 with data fromthe audio component 810 to identify what user's face appears to bespeaking at the same time audio is captured by the local device 110 forpurposes of identifying a user who spoke an input to the local device110.

The local device 110 may include biometric sensors that transmit data tothe biometric component 812. For example, the biometric component 812may receive data corresponding to fingerprints, iris or retina scans,thermal scans, weights of users, a size of a user, pressure (e.g.,within floor sensors), etc., and may determine a biometric profilecorresponding to a user. The biometric component 812 may distinguishbetween a user and sound from a television, for example. Thus, thebiometric component 812 may incorporate biometric information into aconfidence level for determining an identity of a user.

The RF component 814 may use RF localization to track devices that auser may carry or wear. For example, a user may be associated with adevice. The device may emit RF signals (e.g., Wi-Fi, Bluetooth®, etc.).The local device 110 may detect the signal and indicate to the RFcomponent 814 the strength of the signal (e.g., as a received signalstrength indication (RSSI)). The RF component 814 may use the RSSI todetermine an identity of a user (with an associated confidence level).In some instances, the RF component 814 may determine that a received RFsignal is associated with a mobile device that is associated with aparticular user identifier.

In some instances, the local device 110 may include some RF or otherdetection processing capabilities so that a user who speaks an input mayscan, tap, or otherwise acknowledge his/her personal device to the localdevice 110. In this manner, the user may “register” with the localdevice 110 for purposes of the local device 110 determining who spoke aparticular input. Such a registration may occur prior to, during, orafter speaking of an input.

The ML component 816 may track the behavior of various users as a factorin determining a confidence level of the identity of the user. Forexample, a user may adhere to a regular schedule such that the user isat a first location during the day (e.g., at work or at school). In thisexample, the ML component 816 factors in past behavior and/or trendsinto determining the identity of the user that provided input to thelocal device 110. Thus, the ML component 816 may use historical dataand/or usage patterns over time to increase or decrease a confidencelevel of an identity of a user.

In some instances, the recognition confidence component 818 receivesdeterminations from the various components 808, 810, 812, 814, and 816,and may determine a final confidence level associated with the identityof a user. The confidence level or other score data may be included inthe user-recognition data 895.

The audio component 810 may receive data from one or more sensorscapable of providing an audio signal (e.g., one or more microphones) tofacilitate recognizing a user. The audio component 810 may perform audiorecognition on an audio signal to determine an identity of the user andassociated user identifier. In some instances, the audio component 810may perform voice recognition to determine an identity of a user.

The audio component 810 may also perform user identification based onaudio received by the local device 110. The audio component 810 maydetermine scores indicating whether speech in the audio originated fromparticular users. For example, a first score may indicate a likelihoodthat speech in the audio originated from a first user associated with afirst user identifier, a second score may indicate a likelihood thatspeech in the audio originated from a second user associated with asecond user identifier, etc. The audio component 810 may perform userrecognition by comparing audio characteristics representing the audio tostored audio characteristics of users.

FIG. 9 is a block diagram conceptually illustrating a local device 110that may be used with the system. FIG. 10 is a block diagramconceptually illustrating example components of a remote device, such asthe system(s) 120, which may assist with ASR processing, NLU processing,etc., and the skill system(s) 225. The term “server” as used herein mayrefer to a traditional server as understood in a server/client computingstructure but may also refer to a number of different computingcomponents that may assist with the operations discussed herein. Forexample, a server may include one or more physical computing components(such as a rack server) that are connected to other devices/componentseither physically and/or over a network and is capable of performingcomputing operations. A server may also include one or more virtualmachines that emulates a computer system and is run on one or acrossmultiple devices. A server may also include other combinations ofhardware, software, firmware, or the like to perform operationsdiscussed herein. The server(s) may be configured to operate using oneor more of a client-server model, a computer bureau model, gridcomputing techniques, fog computing techniques, mainframe techniques,utility computing techniques, a peer-to-peer model, sandbox techniques,or other computing techniques.

Multiple servers (120/225) may be included in the system, such as one ormore servers 120 for performing ASR processing, one or more servers 120for performing NLU processing, one or more skill system(s) 225 forperforming actions responsive to user inputs, etc. In operation, each ofthese devices (or groups of devices) may include computer-readable andcomputer-executable instructions that reside on the respective device(120/225), as will be discussed further below.

Each of these devices (110/120/225) may include one or morecontrollers/processors (904/1004), which may each include a centralprocessing unit (CPU) for processing data and computer-readableinstructions, and a memory (906/1006) for storing data and instructionsof the respective device. The memories (906/1006) may individuallyinclude volatile random access memory (RAM), non-volatile read onlymemory (ROM), non-volatile magnetoresistive memory (MRAM), and/or othertypes of memory. Each device (110/120/225) may also include a datastorage component (908/1008) for storing data andcontroller/processor-executable instructions. Each data storagecomponent (908/1008) may individually include one or more non-volatilestorage types such as magnetic storage, optical storage, solid-statestorage, etc. Each device (110/120/225) may also be connected toremovable or external non-volatile memory and/or storage (such as aremovable memory card, memory key drive, networked storage, etc.)through respective input/output device interfaces (902/1002).

Computer instructions for operating each device (110/120/225) and itsvarious components may be executed by the respective device'scontroller(s)/processor(s) (904/1004), using the memory (906/1006) astemporary “working” storage at runtime. A device's computer instructionsmay be stored in a non-transitory manner in non-volatile memory(906/1006), storage (908/1008), or an external device(s). Alternatively,some or all of the executable instructions may be embedded in hardwareor firmware on the respective device in addition to or instead ofsoftware.

Each device (110/120/225) includes input/output device interfaces(902/1002). A variety of components may be connected through theinput/output device interfaces (902/1002), as will be discussed furtherbelow. Additionally, each device (110/120/225) may include anaddress/data bus (924/1024) for conveying data among components of therespective device. Each component within a device (110/120/225) may alsobe directly connected to other components in addition to (or instead of)being connected to other components across the bus (924/1024).

Referring to FIG. 9, the local device 110 may include input/outputdevice interfaces 902 that connect to a variety of components such as anaudio output component such as a speaker 912, a wired headset or awireless headset (not illustrated), or other component capable ofoutputting audio. The local device 110 may also include an audio capturecomponent. The audio capture component may be, for example, a microphone920 or array of microphones, a wired headset or a wireless headset (notillustrated), etc. If an array of microphones is included, approximatedistance to a sound's point of origin may be determined by acousticlocalization based on time and amplitude differences between soundscaptured by different microphones of the array. The local device 110 mayadditionally include a display 916 for displaying content. The localdevice 110 may further include a camera 918.

Via antenna(s) 914, the input/output device interfaces 902 may connectto one or more networks 199 via a wireless local area network (WLAN)(such as Wi-Fi) radio, Bluetooth, and/or wireless network radio, such asa radio capable of communication with a wireless communication networksuch as a Long Term Evolution (LTE) network, WiMAX network, 3G network,4G network, 5G network, etc. A wired connection such as Ethernet mayalso be supported. Through the network(s) 199, the system may bedistributed across a networked environment. The I/O device interface(902/1002) may also include communication components that allow data tobe exchanged between devices such as different physical servers in acollection of servers or other components.

The components of the device(s) 110, the system(s) 120, or the skillsystem(s) 225 may include their own dedicated processors, memory, and/orstorage. Alternatively, one or more of the components of the device(s)110, the system(s) 120, or the skill system(s) 225 may utilize the I/Ointerfaces (902/1002), processor(s) (904/1004), memory (906/1006),and/or storage (908/1008) of the device(s) 110 system(s) 120, or theskill system(s) 225, respectively. Thus, the ASR component 250 may haveits own I/O interface(s), processor(s), memory, and/or storage; the NLUcomponent 260 may have its own I/O interface(s), processor(s), memory,and/or storage; and so forth for the various components discussedherein.

As noted above, multiple devices may be employed in a single system. Insuch a multi-device system, each of the devices may include differentcomponents for performing different aspects of the system's processing.The multiple devices may include overlapping components. The componentsof the local device 110, the system(s) 120, and the skill system(s) 225,as described herein, are illustrative, and may be located as astand-alone device or may be included, in whole or in part, as acomponent of a larger device or system.

As illustrated in FIG. 11, multiple devices (110 a-110 g, 120, 225) maycontain components of the system and the devices may be connected over anetwork(s) 199. The network(s) 199 may include a local or privatenetwork or may include a wide network such as the Internet. Devices maybe connected to the network(s) 199 through either wired or wirelessconnections. For example, a vehicle 110 a, a smart phone 110 b, a smartwatch 110 c, a tablet computer 110 d, a vehicle 110 e, a display vehicle110 f, and/or a smart television 110 g may be connected to thenetwork(s) 199 through a wireless service provider, over a Wi-Fi orcellular network connection, or the like. Other devices are included asnetwork-connected support devices, such as the system(s) 120, the skillsystem(s) 225, and/or others. The support devices may connect to thenetwork(s) 199 through a wired connection or wireless connection.Networked devices may capture audio using one-or-more built-in orconnected microphones or other audio capture devices, with processingperformed by ASR components, NLU components, or other components of thesame device or another device connected via the network(s) 199, such asthe ASR component 250, the NLU component 260, etc., of one or moreservers 120.

The concepts disclosed herein may be applied within a number ofdifferent devices and computer systems, including, for example,general-purpose computing systems and distributed computingenvironments.

The above aspects of the present disclosure are meant to beillustrative. They were chosen to explain the principles and applicationof the disclosure and are not intended to be exhaustive or to limit thedisclosure. Many modifications and variations of the disclosed aspectsmay be apparent to those of skill in the art. Persons having ordinaryskill in the field of computers and speech processing should recognizethat components and process steps described herein may beinterchangeable with other components or steps, or combinations ofcomponents or steps, and still achieve the benefits and advantages ofthe present disclosure. Moreover, it should be apparent to one skilledin the art, that the disclosure may be practiced without some or all ofthe specific details and steps disclosed herein.

Aspects of the disclosed system may be implemented as a computer methodor as an article of manufacture such as a memory device ornon-transitory computer readable storage medium. The computer readablestorage medium may be readable by a computer and may compriseinstructions for causing a computer or other device to perform processesdescribed in the present disclosure. The computer readable storagemedium may be implemented by a volatile computer memory, non-volatilecomputer memory, hard drive, solid-state memory, flash drive, removabledisk, and/or other media. In addition, components of system may beimplemented as in firmware or hardware, such as an acoustic front end(AFE), which comprises, among other things, analog and/or digitalfilters (e.g., filters configured as firmware to a digital signalprocessor (DSP)).

Conditional language used herein, such as, among others, “can,” “could,”“might,” “may,” “e.g.,” and the like, unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain embodiments include, whileother embodiments do not include, certain features, elements and/orsteps. Thus, such conditional language is not generally intended toimply that features, elements, and/or steps are in any way required forone or more embodiments or that one or more embodiments necessarilyinclude logic for deciding, with or without other input or prompting,whether these features, elements, and/or steps are included or are to beperformed in any particular embodiment. The terms “comprising,”“including,” “having,” and the like are synonymous and are usedinclusively, in an open-ended fashion, and do not exclude additionalelements, features, acts, operations, and so forth. Also, the term “or”is used in its inclusive sense (and not in its exclusive sense) so thatwhen used, for example, to connect a list of elements, the term “or”means one, some, or all of the elements in the list.

Disjunctive language such as the phrase “at least one of X, Y, Z,”unless specifically stated otherwise, is understood with the context asused in general to present that an item, term, etc., may be either X, Y,or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, suchdisjunctive language is not generally intended to, and should not, implythat certain embodiments require at least one of X, at least one of Y,or at least one of Z to each be present.

As used in this disclosure, the term “a” or “one” may include one ormore items unless specifically stated otherwise. Further, the phrase“based on” is intended to mean “based at least in part on” unlessspecifically stated otherwise.

1.-20. (canceled)
 21. A computer-implemented method, comprising:receiving, from a user device, first audio data representing anutterance; determining that the utterance is intended for a firstspeech-processing configuration; processing the first audio data todetermine a command; determining that the command is associated with asecond speech-processing configuration; performing speech synthesis todetermine second audio data representing a message that the command isassociated with the second speech-processing configuration; and sending,to the user device, the second audio data.
 22. The computer-implementedmethod of claim 21, further comprising: receiving, from the user device,first data representing a wakeword detected by the user device, whereindetermining that the utterance is intended for the firstspeech-processing configuration comprises determining that the wakewordis associated with the first speech-processing configuration.
 23. Thecomputer-implemented method of claim 21, further comprising: determiningoutput data representing text corresponding to the message that thecommand is associated with the second speech-processing configuration,wherein the text includes a name of the second speech-processingconfiguration, wherein performing the speech synthesis uses the outputdata, and wherein the second audio data includes a representation of thename.
 24. The computer-implemented method of claim 21, furthercomprising: causing a component associated with the secondspeech-processing configuration to process data representing theutterance to determine output data corresponding to a response to thecommand.
 25. The computer-implemented method of claim 21, furthercomprising: performing the speech synthesis uses data associated with aspeech synthesis voice associated with the first speech-processingconfiguration.
 26. The computer-implemented method of claim 21, furthercomprising: determining a user account associated with the first audiodata, wherein performing the speech synthesis is based at least in parton data associated with the user account.
 27. The computer-implementedmethod of claim 26, further comprising: determining, based at least inpart on the user account, first data corresponding to at least onehistorical interaction with the second speech-processing configuration,wherein performing the speech synthesis is based at least in part on thefirst data.
 28. The computer-implemented method of claim 21, furthercomprising: selecting first message text to include in the message,wherein the first message text is selected from at least the firstmessage text and second message text, the second message text includinga different group of words from the first message text.
 29. Thecomputer-implemented method of claim 28, wherein the selecting is basedat least in part on a user account associated with the first audio data.30. The computer-implemented method of claim 21, wherein: the messagecomprises a request for permission to use the second speech-processingconfiguration to process the command; and the method further comprises:receiving third audio data, processing the third audio data to determinean indication of permission to use the second speech-processingconfiguration, and based at least in part on the indication ofpermission, causing a component associated with the secondspeech-processing configuration to process data representing theutterance to determine output data corresponding to a response to thecommand.
 31. A system comprising: at least one processor; and at leastone memory comprising instructions that, when executed by the at leastone processor, cause the system to: receive, from a user device, firstaudio data representing an utterance; determine that the utterance isintended for a first speech-processing configuration; process the firstaudio data to determine a command; determine that the command isassociated with a second speech-processing configuration; perform speechsynthesis to determine second audio data representing a message that thecommand is associated with the second speech-processing configuration;and send, to the user device, the second audio data.
 32. The system ofclaim 31, wherein the at least one memory further comprises instructionsthat, when executed by the at least one processor, further cause thesystem to: receive, from the user device, first data representing awakeword detected by the user device, wherein the instructions thatcause the system to determine that the utterance is intended for thefirst speech-processing configuration comprise instructions that, whenexecuted by the at least one processor, cause the system to determinethat the wakeword is associated with the first speech-processingconfiguration.
 33. The system of claim 31, wherein the at least onememory further comprises instructions that, when executed by the atleast one processor, further cause the system to: determine output datarepresenting text corresponding to the message that the command isassociated with the second speech-processing configuration, wherein thetext includes a name of the second speech-processing configuration,wherein the instructions that cause the system to perform the speechsynthesis use the output data, and wherein the second audio dataincludes a representation of the name.
 34. The system of claim 31,wherein the at least one memory further comprises instructions that,when executed by the at least one processor, further cause the systemto: cause a component associated with the second speech-processingconfiguration to process data representing the utterance to determineoutput data corresponding to a response to the command.
 35. The systemof claim 31, wherein the at least one memory further comprisesinstructions that, when executed by the at least one processor, furthercause the system to: perform the speech synthesis uses data associatedwith a speech synthesis voice associated with the firstspeech-processing configuration.
 36. The system of claim 31, wherein theat least one memory further comprises instructions that, when executedby the at least one processor, further cause the system to: determine auser account associated with the first audio data, wherein theinstructions that cause the system to perform the speech synthesis arebased at least in part on data associated with the user account.
 37. Thesystem of claim 36, wherein the at least one memory further comprisesinstructions that, when executed by the at least one processor, furthercause the system to: determine, based at least in part on the useraccount, first data corresponding to at least one historical interactionwith the second speech-processing configuration, wherein theinstructions that cause the system to perform the speech synthesis arebased at least in part on the first data.
 38. The system of claim 31,wherein the at least one memory further comprises instructions that,when executed by the at least one processor, further cause the systemto: select first message text to include in the message, wherein thefirst message text is selected from at least the first message text andsecond message text, the second message text including a different groupof words from the first message text.
 39. The system of claim 38,wherein the selecting is based at least in part on a user accountassociated with the first audio data.
 40. The system of claim 31,wherein: the message comprises a request for permission to use thesecond speech-processing configuration to process the command; and theat least one memory further comprises instructions that, when executedby the at least one processor, further cause the system to: receivethird audio data, process the third audio data to determine anindication of permission to use the second speech-processingconfiguration, and based at least in part on the indication ofpermission, cause a component associated with the secondspeech-processing configuration to process data representing theutterance to determine output data corresponding to a response to thecommand.